Journal articles on the topic 'Privacy-preserving federated learning algorithms'
To see the other types of publications on this topic, follow the link: Privacy-preserving federated learning algorithms.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Privacy-preserving federated learning algorithms.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Cellamare, Matteo, Anna J. van Gestel, Hasan Alradhi, Frank Martin, and Arturo Moncada-Torres. "A Federated Generalized Linear Model for Privacy-Preserving Analysis." Algorithms 15, no. 7 (July 13, 2022): 243. http://dx.doi.org/10.3390/a15070243.
Full textAbstract:
In the last few years, federated learning (FL) has emerged as a novel alternative for analyzing data spread across different parties without needing to centralize them. In order to increase the adoption of FL, there is a need to develop more algorithms that can be deployed under this novel privacy-preserving paradigm. In this paper, we present our federated generalized linear model (GLM) for horizontally partitioned data. It allows generating models of different families (linear, Poisson, logistic) without disclosing privacy-sensitive individual records. We describe its algorithm (which can be implemented in the user’s platform of choice) and compare the obtained federated models against their centralized counterpart, which were mathematically equivalent. We also validated their execution time with increasing numbers of records and involved parties. We show that our federated GLM is accurate enough to be used for the privacy-preserving analysis of horizontally partitioned data in real-life scenarios. Further development of this type of algorithm has the potential to make FL a much more common practice among researchers.
2
Park, Jaehyoung, and Hyuk Lim. "Privacy-Preserving Federated Learning Using Homomorphic Encryption." Applied Sciences 12, no. 2 (January 12, 2022): 734. http://dx.doi.org/10.3390/app12020734.
Full textAbstract:
Federated learning (FL) is a machine learning technique that enables distributed devices to train a learning model collaboratively without sharing their local data. FL-based systems can achieve much stronger privacy preservation since the distributed devices deliver only local model parameters trained with local data to a centralized server. However, there exists a possibility that a centralized server or attackers infer/extract sensitive private information using the structure and parameters of local learning models. We propose employing homomorphic encryption (HE) scheme that can directly perform arithmetic operations on ciphertexts without decryption to protect the model parameters. Using the HE scheme, the proposed privacy-preserving federated learning (PPFL) algorithm enables the centralized server to aggregate encrypted local model parameters without decryption. Furthermore, the proposed algorithm allows each node to use a different HE private key in the same FL-based system using a distributed cryptosystem. The performance analysis and evaluation of the proposed PPFL algorithm are conducted in various cloud computing-based FL service scenarios.
3
Thorgeirsson, Adam Thor, and Frank Gauterin. "Probabilistic Predictions with Federated Learning." Entropy 23, no. 1 (December 30, 2020): 41. http://dx.doi.org/10.3390/e23010041.
Full textAbstract:
Probabilistic predictions with machine learning are important in many applications. These are commonly done with Bayesian learning algorithms. However, Bayesian learning methods are computationally expensive in comparison with non-Bayesian methods. Furthermore, the data used to train these algorithms are often distributed over a large group of end devices. Federated learning can be applied in this setting in a communication-efficient and privacy-preserving manner but does not include predictive uncertainty. To represent predictive uncertainty in federated learning, our suggestion is to introduce uncertainty in the aggregation step of the algorithm by treating the set of local weights as a posterior distribution for the weights of the global model. We compare our approach to state-of-the-art Bayesian and non-Bayesian probabilistic learning algorithms. By applying proper scoring rules to evaluate the predictive distributions, we show that our approach can achieve similar performance as the benchmark would achieve in a non-distributed setting.
4
Jiang, Xue, Xuebing Zhou, and Jens Grossklags. "Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder." Proceedings on Privacy Enhancing Technologies 2022, no. 1 (November 20, 2021): 481–500. http://dx.doi.org/10.2478/popets-2022-0024.
Full textAbstract:
Abstract Business intelligence and AI services often involve the collection of copious amounts of multidimensional personal data. Since these data usually contain sensitive information of individuals, the direct collection can lead to privacy violations. Local differential privacy (LDP) is currently considered a state-ofthe-art solution for privacy-preserving data collection. However, existing LDP algorithms are not applicable to high-dimensional data; not only because of the increase in computation and communication cost, but also poor data utility. In this paper, we aim at addressing the curse-of-dimensionality problem in LDP-based high-dimensional data collection. Based on the idea of machine learning and data synthesis, we propose DP-Fed-Wae, an efficient privacy-preserving framework for collecting high-dimensional categorical data. With the combination of a generative autoencoder, federated learning, and differential privacy, our framework is capable of privately learning the statistical distributions of local data and generating high utility synthetic data on the server side without revealing users’ private information. We have evaluated the framework in terms of data utility and privacy protection on a number of real-world datasets containing 68–124 classification attributes. We show that our framework outperforms the LDP-based baseline algorithms in capturing joint distributions and correlations of attributes and generating high-utility synthetic data. With a local privacy guarantee ∈ = 8, the machine learning models trained with the synthetic data generated by the baseline algorithm cause an accuracy loss of 10% ~ 30%, whereas the accuracy loss is significantly reduced to less than 3% and at best even less than 1% with our framework. Extensive experimental results demonstrate the capability and efficiency of our framework in synthesizing high-dimensional data while striking a satisfactory utility-privacy balance.
5
Zhou, Zhou, Youliang Tian, and Changgen Peng. "Privacy-Preserving Federated Learning Framework with General Aggregation and Multiparty Entity Matching." Wireless Communications and Mobile Computing 2021 (June 26, 2021): 1–14. http://dx.doi.org/10.1155/2021/6692061.
Full textAbstract:
The requirement for data sharing and privacy has brought increasing attention to federated learning. However, the existing aggregation models are too specialized and deal less with users’ withdrawal issue. Moreover, protocols for multiparty entity matching are rarely covered. Thus, there is no systematic framework to perform federated learning tasks. In this paper, we systematically propose a privacy-preserving federated learning framework (PFLF) where we first construct a general secure aggregation model in federated learning scenarios by combining the Shamir secret sharing with homomorphic cryptography to ensure that the aggregated value can be decrypted correctly only when the number of participants is greater than t . Furthermore, we propose a multiparty entity matching protocol by employing secure multiparty computing to solve the entity alignment problems and a logistic regression algorithm to achieve privacy-preserving model training and support the withdrawal of users in vertical federated learning (VFL) scenarios. Finally, the security analyses prove that PFLF preserves the data privacy in the honest-but-curious model, and the experimental evaluations show PFLF attains consistent accuracy with the original model and demonstrates the practical feasibility.
6
Gong, Xuan, Abhishek Sharma, Srikrishna Karanam, Ziyan Wu, Terrence Chen, David Doermann, and Arun Innanje. "Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 11891–99. http://dx.doi.org/10.1609/aaai.v36i11.21446.
Full textAbstract:
Federated Learning (FL) is a machine learning paradigm where local nodes collaboratively train a central model while the training data remains decentralized. Existing FL methods typically share model parameters or employ co-distillation to address the issue of unbalanced data distribution. However, they suffer from communication bottlenecks. More importantly, they risk privacy leakage risk. In this work, we develop a privacy preserving and communication efficient method in a FL framework with one-shot offline knowledge distillation using unlabeled, cross-domain, non-sensitive public data. We propose a quantized and noisy ensemble of local predictions from completely trained local models for stronger privacy guarantees without sacrificing accuracy. Based on extensive experiments on image classification and text classification tasks, we show that our method outperforms baseline FL algorithms with superior performance in both accuracy and data privacy preservation.
7
Loftus, Tyler J., Matthew M. Ruppert, Benjamin Shickel, Tezcan Ozrazgat-Baslanti, Jeremy A. Balch, Philip A. Efron, Gilbert R. Upchurch, et al. "Federated learning for preserving data privacy in collaborative healthcare research." DIGITAL HEALTH 8 (January 2022): 205520762211344. http://dx.doi.org/10.1177/20552076221134455.
Full textAbstract:
Generalizability, external validity, and reproducibility are high priorities for artificial intelligence applications in healthcare. Traditional approaches to addressing these elements involve sharing patient data between institutions or practice settings, which can compromise data privacy (individuals’ right to prevent the sharing and disclosure of information about themselves) and data security (simultaneously preserving confidentiality, accuracy, fidelity, and availability of data). This article describes insights from real-world implementation of federated learning techniques that offer opportunities to maintain both data privacy and availability via collaborative machine learning that shares knowledge, not data. Local models are trained separately on local data. As they train, they send local model updates (e.g. coefficients or gradients) for consolidation into a global model. In some use cases, global models outperform local models on new, previously unseen local datasets, suggesting that collaborative learning from a greater number of examples, including a greater number of rare cases, may improve predictive performance. Even when sharing model updates rather than data, privacy leakage can occur when adversaries perform property or membership inference attacks which can be used to ascertain information about the training set. Emerging techniques mitigate risk from adversarial attacks, allowing investigators to maintain both data privacy and availability in collaborative healthcare research. When data heterogeneity between participating centers is high, personalized algorithms may offer greater generalizability by improving performance on data from centers with proportionately smaller training sample sizes. Properly applied, federated learning has the potential to optimize the reproducibility and performance of collaborative learning while preserving data security and privacy.
8
Fang, Haokun, and Quan Qian. "Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning." Future Internet 13, no. 4 (April 8, 2021): 94. http://dx.doi.org/10.3390/fi13040094.
Full textAbstract:
Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated learning. The core idea is all learning parties just transmitting the encrypted gradients by homomorphic encryption. From experiments, the model trained by PFMLP has almost the same accuracy, and the deviation is less than 1%. Considering the computational overhead of homomorphic encryption, we use an improved Paillier algorithm which can speed up the training by 25–28%. Moreover, comparisons on encryption key length, the learning network structure, number of learning clients, etc. are also discussed in detail in the paper.
9
Ali, Waqar, Rajesh Kumar, Zhiyi Deng, Yansong Wang, and Jie Shao. "A Federated Learning Approach for Privacy Protection in Context-Aware Recommender Systems." Computer Journal 64, no. 7 (April 30, 2021): 1016–27. http://dx.doi.org/10.1093/comjnl/bxab025.
Full textAbstract:
Abstract Privacy protection is one of the key concerns of users in recommender system-based consumer markets. Popular recommendation frameworks such as collaborative filtering (CF) suffer from several privacy issues. Federated learning has emerged as an optimistic approach for collaborative and privacy-preserved learning. Users in a federated learning environment train a local model on a self-maintained item log and collaboratively train a global model by exchanging model parameters instead of personalized preferences. In this research, we proposed a federated learning-based privacy-preserving CF model for context-aware recommender systems that work with a user-defined collaboration protocol to ensure users’ privacy. Instead of crawling users’ personal information into a central server, the whole data are divided into two disjoint parts, i.e. user data and sharable item information. The inbuilt power of federated architecture ensures the users’ privacy concerns while providing considerably accurate recommendations. We evaluated the performance of the proposed algorithm with two publicly available datasets through both the prediction and ranking perspectives. Despite the federated cost and lack of open collaboration, the overall performance achieved through the proposed technique is comparable with popular recommendation models and satisfactory while providing significant privacy guarantees.
10
Wang, Shengsheng, Shuzhen Lu, and Bin Cao. "Medical Image Object Detection Algorithm for Privacy-Preserving Federated Learning." Journal of Computer-Aided Design & Computer Graphics 33, no. 10 (October 1, 2021): 1153–562. http://dx.doi.org/10.3724/sp.j.1089.2021.18416.
Full text
11
Zhao, Jianzhe, Mengbo Yang, Ronglin Zhang, Wuganjing Song, Jiali Zheng, Jingran Feng, and Stan Matwin. "Privacy-Enhanced Federated Learning: A Restrictively Self-Sampled and Data-Perturbed Local Differential Privacy Method." Electronics 11, no. 23 (December 2, 2022): 4007. http://dx.doi.org/10.3390/electronics11234007.
Full textAbstract:
As a popular distributed learning framework, federated learning (FL) enables clients to conduct cooperative training without sharing data, thus having higher security and enjoying benefits in processing large-scale, high-dimensional data. However, by sharing parameters in the federated learning process, the attacker can still obtain private information from the sensitive data of participants by reverse parsing. Local differential privacy (LDP) has recently worked well in preserving privacy for federated learning. However, it faces the inherent problem of balancing privacy, model performance, and algorithm efficiency. In this paper, we propose a novel privacy-enhanced federated learning framework (Optimal LDP-FL) which achieves local differential privacy protection by the client self-sampling and data perturbation mechanisms. We theoretically analyze the relationship between the model accuracy and client self-sampling probability. Restrictive client self-sampling technology is proposed which eliminates the randomness of the self-sampling probability settings in existing studies and improves the utilization of the federated system. A novel, efficiency-optimized LDP data perturbation mechanism (Adaptive-Harmony) is also proposed, which allows an adaptive parameter range to reduce variance and improve model accuracy. Comprehensive experiments on the MNIST and Fashion MNIST datasets show that the proposed method can significantly reduce computational and communication costs with the same level of privacy and model utility.
12
Wang, Jie, Li Tian, Guowei Zhu, Chang Liu, and Feng Long. "Indoor Positioning Privacy Protection Method Based on Federated Learning in MEC Environment." Mobile Information Systems 2022 (October 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/2311264.
Full textAbstract:
Under the current background, it is very important to study the key technologies of new power system edge-to-side security protection for massive heterogeneous power IoT terminals and edge IoT agents, including defense technologies at the levels of device ontology security, communication interaction security, and secure access. Meaning. The new power system edge-to-side security protection technology has a summary impact on the privacy protection of indoor positioning. This paper proposes an indoor positioning privacy protection method based on federated learning in Mobile Edge. Computing (MEC) environment. Firstly, we analyze the learning mechanisms of horizontal, vertical, and transfer-federated learning, respectively, and mathematically describe it based on the applicability of horizontal and vertical-federated learning under different sample data characteristics. Then, the risk of data leakage when data are used for research or analysis is greatly reduced by introducing differential privacy. In addition, considering the positioning performance, privacy protection, and resource overhead, we further propose an indoor positioning privacy protection model based on federated learning and corresponding algorithms in MEC environment. Finally, through simulation experiments, the proposed algorithm and other three algorithms are, respectively, compared and analyzed in the case of two identical datasets. The experimental results show that the convergence speed, localization time consumption, and localization accuracy of the proposed algorithm are all optimal. Moreover, its final positioning accuracy is about 94%, the average positioning time is 250 ms, and the performance is better than the other three comparison algorithms.
13
Kjamilji, Artrim. "Techniques and Challenges while Applying Machine Learning Algorithms in Privacy Preserving Fashion." Proceeding International Conference on Science and Engineering 3 (April 30, 2020): xix. http://dx.doi.org/10.14421/icse.v3.600.
Full textAbstract:
Nowadays many different entities collect data of the same nature, but in slightly different environments. In this sense different hospitals collect data about their patients’ symptoms and corresponding disease diagnoses, different banks collect transactions of their customers’ bank accounts, multiple cyber-security companies collect data about log files and corresponding attacks, etc. It is shown that if those different entities would merge their privately collected data in a single dataset and use it to train a machine learning (ML) model, they often end up with a trained model that outperforms the human experts of the corresponding fields in terms of accurate predictions. However, there is a drawback. Due to privacy concerns, empowered by laws and ethical reasons, no entity is willing to share with others their privately collected data. The same problem appears during the classification case over an already trained ML model. On one hand, a user that has an unclassified query (record), doesn’t want to share with the server that owns the trained model neither the content of the query (which might contain private data such as credit card number, IP address, etc.), nor the final prediction (classification) of the query. On the other hand, the owner of the trained model doesn’t want to leak any parameter of the trained model to the user. In order to overcome those shortcomings, several cryptographic and probabilistic techniques have been proposed during the last few years to enable both privacy preserving training and privacy preserving classification schemes. Some of them include anonymization and k-anonymity, differential privacy, secure multiparty computation (MPC), federated learning, Private Information Retrieval (PIR), Oblivious Transfer (OT), garbled circuits and/or homomorphic encryption, to name a few. Theoretical analyses and experimental results show that the current privacy preserving schemes are suitable for real-case deployment, while the accuracy of most of them differ little or not at all with the schemes that work in non-privacy preserving fashion.
14
Tursunboev, Jamshid, Yong-Sung Kang, Sung-Bum Huh, Dong-Woo Lim, Jae-Mo Kang, and Heechul Jung. "Hierarchical Federated Learning for Edge-Aided Unmanned Aerial Vehicle Networks." Applied Sciences 12, no. 2 (January 11, 2022): 670. http://dx.doi.org/10.3390/app12020670.
Full textAbstract:
Federated learning (FL) allows UAVs to collaboratively train a globally shared machine learning model while locally preserving their private data. Recently, the FL in edge-aided unmanned aerial vehicle (UAV) networks has drawn an upsurge of research interest due to a bursting increase in heterogeneous data acquired by UAVs and the need to build the global model with privacy; however, a critical issue is how to deal with the non-independent and identically distributed (non-i.i.d.) nature of heterogeneous data while ensuring the convergence of learning. To effectively address this challenging issue, this paper proposes a novel and high-performing FL scheme, namely, the hierarchical FL algorithm, for the edge-aided UAV network, which exploits the edge servers located in base stations as intermediate aggregators with employing commonly shared data. Experiment results demonstrate that the proposed hierarchical FL algorithm outperforms several baseline FL algorithms and exhibits better convergence behavior.
15
Berghout, Tarek, Toufik Bentrcia, Mohamed Amine Ferrag, and Mohamed Benbouzid. "A Heterogeneous Federated Transfer Learning Approach with Extreme Aggregation and Speed." Mathematics 10, no. 19 (September 28, 2022): 3528. http://dx.doi.org/10.3390/math10193528.
Full textAbstract:
Federated learning (FL) is a data-privacy-preserving, decentralized process that allows local edge devices of smart infrastructures to train a collaborative model independently while keeping data localized. FL algorithms, encompassing a well-structured average of the training parameters (e.g., the weights and biases resulting from training-based stochastic gradient descent variants), are subject to many challenges, namely expensive communication, systems heterogeneity, statistical heterogeneity, and privacy concerns. In this context, our paper targets the four aforementioned challenges while focusing on reducing communication and computational costs by involving recursive least squares (RLS) training rules. Accordingly, to the best of our knowledge, this is the first time that the RLS algorithm is modified to completely accommodate non-independent and identically distributed data (non-IID) for federated transfer learning (FTL). Furthermore, this paper also introduces a newly generated dataset capable of emulating such real conditions and of making data investigation available on ordinary commercial computers with quad-core microprocessors and less need for higher computing hardware. Applications of FTL-RLS on the generated data under different levels of complexity closely related to different levels of cardinality lead to a variety of conclusions supporting its performance for future uses.
16
Hongbin, Fan, and Zhou Zhi. "Privacy-Preserving Data Aggregation Scheme Based on Federated Learning for IIoT." Mathematics 11, no. 1 (January 1, 2023): 214. http://dx.doi.org/10.3390/math11010214.
Full textAbstract:
The extensive application of the Internet of Things in the industrial field has formed the industrial Internet of Things (IIoT). By analyzing and training data from the industrial Internet of Things, intelligent manufacturing can be realized. Due to privacy concerns, the industrial data of various institutions cannot be shared, which forms data islands. To address this challenge, we propose a privacy-preserving data aggregation federated learning (PPDAFL) scheme for the IIoT. In federated learning, data aggregation is adopted to protect model changes and provide data security for industrial devices. By utilizing a practical Byzantine fault tolerance (PBFT) algorithm, each round selects an IIoT device from each aggregation area as the data aggregation and initialization node, and uses data aggregation to protect the model changes of a single user while resisting reverse analysis attacks from the industrial management center. The Paillier cryptosystem and secret sharing are combined to realize data security, fault tolerance, and data sharing. A security analysis and performance evaluation show that the scheme reduces computation and communication overheads while guaranteeing data privacy, message authenticity, and integrity.
17
Raisaro, J. L., Francesco Marino, Juan Troncoso-Pastoriza, Raphaelle Beau-Lejdstrom, Riccardo Bellazzi, Robert Murphy, Elmer V. Bernstam, et al. "SCOR: A secure international informatics infrastructure to investigate COVID-19." Journal of the American Medical Informatics Association 27, no. 11 (July 10, 2020): 1721–26. http://dx.doi.org/10.1093/jamia/ocaa172.
Full textAbstract:
Abstract Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions. An international group of passionate researchers came together with a joint mission to solve the problem with our finest models and tools. The SCOR Consortium has developed a ready-to-deploy secure infrastructure using world-class privacy and security technologies to reconcile the privacy/utility conflicts. We hope our effort will make a change and accelerate research in future pandemics with broad and diverse samples on an international scale.
18
Kumar, Dheeraj. "FL-NoiseMap: A Federated Learning-based privacy-preserving Urban Noise-Pollution Measurement System." Noise Mapping 9, no. 1 (January 1, 2022): 128–45. http://dx.doi.org/10.1515/noise-2022-0153.
Full textAbstract:
Abstract Increasing levels of noise pollution in urban environments are a primary cause of various physical and psychological health issues. There is an urgent requirement to manage environmental noise by assessing the current levels of noise pollution by gathering real-world data and building a fine-granularity real-time noise map. Traditionally, simulation-based, small-scale sensor-network-based, and participatory sensing-based approaches have been used to estimate noise levels in urban areas. These techniques are inadequate to gauge the prevalence of noise pollution in urban areas and have been shown to leak private user data. This paper proposes a novel federated learning-based urban noise mapping system, FL-NoiseMap, that significantly enhances the privacy of participating users without adversely affecting the application performance. We list several state-of-the-art urban noise monitoring systems that can be seamlessly ported to the federated learning-based paradigm and show that the existing privacy-preserving approaches can be used as an add-on to enhance participants’ privacy. Moreover, we design an “m-hop” application model modification approach for privacy preservation, unique to FL-NoiseMap. We also describe techniques to maintain data reliability for the proposed application. Numerical experiments on simulated datasets showcase the superiority of the proposed scheme in terms of users’ privacy preservation and noise map reliability. The proposed scheme achieves the lowest average normalized root mean square error in the range of 4% to 7% as the number of participants varies between 500 and 5000 while providing maximum coverage of over 95% among various competing algorithms. The proposed malicious contribution removal framework can decrease the average normalizedroot mean square error by more than 50% for simulations having up to 20% malicious users.
19
Peng, Yongqiang, Zongyao Chen, Zexuan Chen, Wei Ou, Wenbao Han, and Jianqiang Ma. "BFLP: An Adaptive Federated Learning Framework for Internet of Vehicles." Mobile Information Systems 2021 (March 2, 2021): 1–18. http://dx.doi.org/10.1155/2021/6633332.
Full textAbstract:
Applications of Internet of Vehicles (IoV) make the life of human beings more intelligent and convenient. However, in the present, there are some problems in IoV, such as data silos and poor privacy preservation. To address the challenges in IoV, we propose a blockchain-based federated learning pool (BFLP) framework. BFLP allows the models to be trained without sharing raw data, and it can choose the most suitable federated learning method according to actual application scenarios. Considering the poor computing power of vehicle systems, we construct a lightweight encryption algorithm called CPC to protect privacy. To verify the proposed framework, we conducted experiments in obstacle-avoiding and traffic forecast scenarios. The results show that the proposed framework can effectively protect the user's privacy, and it is more stable and efficient compared with traditional machine learning technique. Also, we compare the CPC algorithm with other encryption algorithms. And the results show that its calculation cost is much lower compared to other symmetric encryption algorithms.
20
Subramanian, Malliga, Vani Rajasekar, Sathishkumar V. E., Kogilavani Shanmugavadivel, and P. S. Nandhini. "Effectiveness of Decentralized Federated Learning Algorithms in Healthcare: A Case Study on Cancer Classification." Electronics 11, no. 24 (December 10, 2022): 4117. http://dx.doi.org/10.3390/electronics11244117.
Full textAbstract:
Deep learning-based medical image analysis is an effective and precise method for identifying various cancer types. However, due to concerns over patient privacy, sharing diagnostic images across medical facilities is typically not permitted. Federated learning (FL) tries to construct a shared model across dispersed clients under such privacy-preserving constraints. Although there is a good chance of success, dealing with non-IID (non-independent and identical distribution) client data, which is a typical circumstance in real-world FL tasks, is still difficult for FL. We use two FL algorithms, FedAvg and FedProx, to manage client heterogeneity and non-IID data in a federated setting. A heterogeneous data split of the cancer datasets with three different forms of cancer—cervical, lung, and colon—is used to validate the efficacy of the FL. In addition, since hyperparameter optimization presents new difficulties in an FL setting, we also examine the impact of various hyperparameter values. We use Bayesian optimization to fine-tune the hyperparameters and identify the appropriate values in order to increase performance. Furthermore, we investigate the hyperparameter optimization in both local and global models of the FL environment. Through a series of experiments, we find that FedProx outperforms FedAvg in scenarios with significant levels of heterogeneity.
21
Li, Xiaochen, Yuke Hu, Weiran Liu, Hanwen Feng, Li Peng, Yuan Hong, Kui Ren, and Zhan Qin. "OpBoost." Proceedings of the VLDB Endowment 16, no. 2 (October 2022): 202–15. http://dx.doi.org/10.14778/3565816.3565823.
Full textAbstract:
Vertical Federated Learning (FL) is a new paradigm that enables users with non-overlapping attributes of the same data samples to jointly train a model without directly sharing the raw data. Nevertheless, recent works show that it's still not sufficient to prevent privacy leakage from the training process or the trained model. This paper focuses on studying the privacy-preserving tree boosting algorithms under the vertical FL. The existing solutions based on cryptography involve heavy computation and communication overhead and are vulnerable to inference attacks. Although the solution based on Local Differential Privacy (LDP) addresses the above problems, it leads to the low accuracy of the trained model. This paper explores to improve the accuracy of the widely deployed tree boosting algorithms satisfying differential privacy under vertical FL. Specifically, we introduce a framework called OpBoost. Three order-preserving desensitization algorithms satisfying a variant of LDP called distance-based LDP (dLDP) are designed to desensitize the training data. In particular, we optimize the dLDP definition and study efficient sampling distributions to further improve the accuracy and efficiency of the proposed algorithms. The proposed algorithms provide a trade-off between the privacy of pairs with large distance and the utility of desensitized values. Comprehensive evaluations show that OpBoost has a better performance on prediction accuracy of trained models compared with existing LDP approaches on reasonable settings. Our code is open source.
22
Wang, Weiya, Geng Yang, Lin Bao, Ke Ma, and Hao Zhou. "A Privacy-Preserving Crowd Flow Prediction Framework Based on Federated Learning during Epidemics." Security and Communication Networks 2022 (October 26, 2022): 1–20. http://dx.doi.org/10.1155/2022/8712597.
Full textAbstract:
Predicting and managing the movement of people in a region during epidemics’ outbreak is an important step in preventing outbreaks. The protection of user privacy during the outbreak has become a matter of public concern in recent years, yet deep learning models based on datasets collected from mobile devices may pose privacy and security issues. Therefore, how to develop an accurate crowd flow prediction while preserving privacy is a significant problem to be solved, and there is a tradeoff between these two objectives. In this paper, we propose a privacy-preserving mobility prediction framework via federated learning (CFPF) to solve this problem without significantly sacrificing the prediction performance. In this framework, we designed a deep and embedding learning approach called “Multi-Factors CNN-LSTM” (MFCL) that can help to explicitly learn from human trajectory data (weather, holidays, temperature, and POI) during epidemics. Furthermore, we improve the existing federated learning framework by introducing a clustering algorithm to classify clients with similar spatio-temporal characteristics into the same cluster, and select servers at the center of the cluster as edge central servers to integrate the optimal model for each cluster and improve the prediction accuracy. To address the privacy concerns, we introduce local differential privacy into the FL framework which can facilitate collaborative learning with uploaded gradients from users instead of sharing users’ raw data. Finally, we conduct extensive experiments on a realistic crowd flow dataset to evaluate the performance of our CFPF and make a comparison with other existing models. The experimental results demonstrate that our solution can not only achieve accurate crowd flow prediction but also provide a strong privacy guarantee at the same time.
23
Zerka, Fadila, Samir Barakat, Sean Walsh, Marta Bogowicz, Ralph T. H. Leijenaar, Arthur Jochems, Benjamin Miraglio, David Townend, and Philippe Lambin. "Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care." JCO Clinical Cancer Informatics, no. 4 (September 2020): 184–200. http://dx.doi.org/10.1200/cci.19.00047.
Full textAbstract:
Big data for health care is one of the potential solutions to deal with the numerous challenges of health care, such as rising cost, aging population, precision medicine, universal health coverage, and the increase of noncommunicable diseases. However, data centralization for big data raises privacy and regulatory concerns. Covered topics include (1) an introduction to privacy of patient data and distributed learning as a potential solution to preserving these data, a description of the legal context for patient data research, and a definition of machine/deep learning concepts; (2) a presentation of the adopted review protocol; (3) a presentation of the search results; and (4) a discussion of the findings, limitations of the review, and future perspectives. Distributed learning from federated databases makes data centralization unnecessary. Distributed algorithms iteratively analyze separate databases, essentially sharing research questions and answers between databases instead of sharing the data. In other words, one can learn from separate and isolated datasets without patient data ever leaving the individual clinical institutes. Distributed learning promises great potential to facilitate big data for medical application, in particular for international consortiums. Our purpose is to review the major implementations of distributed learning in health care.
24
Asad, Muhammad, Ahmed Moustafa, and Takayuki Ito. "FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning." Applied Sciences 10, no. 8 (April 21, 2020): 2864. http://dx.doi.org/10.3390/app10082864.
Full textAbstract:
Artificial Intelligence (AI) has been applied to solve various challenges of real-world problems in recent years. However, the emergence of new AI technologies has brought several problems, especially with regard to communication efficiency, security threats and privacy violations. Towards this end, Federated Learning (FL) has received widespread attention due to its ability to facilitate the collaborative training of local learning models without compromising the privacy of data. However, recent studies have shown that FL still consumes considerable amounts of communication resources. These communication resources are vital for updating the learning models. In addition, the privacy of data could still be compromised once sharing the parameters of the local learning models in order to update the global model. Towards this end, we propose a new approach, namely, Federated Optimisation (FedOpt) in order to promote communication efficiency and privacy preservation in FL. In order to implement FedOpt, we design a novel compression algorithm, namely, Sparse Compression Algorithm (SCA) for efficient communication, and then integrate the additively homomorphic encryption with differential privacy to prevent data from being leaked. Thus, the proposed FedOpt smoothly trade-offs communication efficiency and privacy preservation in order to adopt the learning task. The experimental results demonstrate that FedOpt outperforms the state-of-the-art FL approaches. In particular, we consider three different evaluation criteria; model accuracy, communication efficiency and computation overhead. Then, we compare the proposed FedOpt with the baseline configurations and the state-of-the-art approaches, i.e., Federated Averaging (FedAvg) and the paillier-encryption based privacy-preserving deep learning (PPDL) on all these three evaluation criteria. The experimental results show that FedOpt is able to converge within fewer training epochs and a smaller privacy budget.
25
Späth, Julian, Julian Matschinske, Frederick K. Kamanu, Sabina A. Murphy, Olga Zolotareva, Mohammad Bakhtiari, Elliott M. Antman, et al. "Privacy-aware multi-institutional time-to-event studies." PLOS Digital Health 1, no. 9 (September 6, 2022): e0000101. http://dx.doi.org/10.1371/journal.pdig.0000101.
Full textAbstract:
Clinical time-to-event studies are dependent on large sample sizes, often not available at a single institution. However, this is countered by the fact that, particularly in the medical field, individual institutions are often legally unable to share their data, as medical data is subject to strong privacy protection due to its particular sensitivity. But the collection, and especially aggregation into centralized datasets, is also fraught with substantial legal risks and often outright unlawful. Existing solutions using federated learning have already demonstrated considerable potential as an alternative for central data collection. Unfortunately, current approaches are incomplete or not easily applicable in clinical studies owing to the complexity of federated infrastructures. This work presents privacy-aware and federated implementations of the most used time-to-event algorithms (survival curve, cumulative hazard rate, log-rank test, and Cox proportional hazards model) in clinical trials, based on a hybrid approach of federated learning, additive secret sharing, and differential privacy. On several benchmark datasets, we show that all algorithms produce highly similar, or in some cases, even identical results compared to traditional centralized time-to-event algorithms. Furthermore, we were able to reproduce the results of a previous clinical time-to-event study in various federated scenarios. All algorithms are accessible through the intuitive web-app Partea (https://partea.zbh.uni-hamburg.de), offering a graphical user interface for clinicians and non-computational researchers without programming knowledge. Partea removes the high infrastructural hurdles derived from existing federated learning approaches and removes the complexity of execution. Therefore, it is an easy-to-use alternative to central data collection, reducing bureaucratic efforts but also the legal risks associated with the processing of personal data to a minimum.
26
Chen, Zunming, Hongyan Cui, Ensen Wu, and Xi Yu. "Dynamic Asynchronous Anti Poisoning Federated Deep Learning with Blockchain-Based Reputation-Aware Solutions." Sensors 22, no. 2 (January 17, 2022): 684. http://dx.doi.org/10.3390/s22020684.
Full textAbstract:
As promising privacy-preserving machine learning technology, federated learning enables multiple clients to train the joint global model via sharing model parameters. However, inefficiency and vulnerability to poisoning attacks significantly reduce federated learning performance. To solve the aforementioned issues, we propose a dynamic asynchronous anti poisoning federated deep learning framework to pursue both efficiency and security. This paper proposes a lightweight dynamic asynchronous algorithm considering the averaging frequency control and parameter selection for federated learning to speed up model averaging and improve efficiency, which enables federated learning to adaptively remove the stragglers with low computing power, bad channel conditions, or anomalous parameters. In addition, a novel local reliability mutual evaluation mechanism is presented to enhance the security of poisoning attacks, which enables federated learning to detect the anomalous parameter of poisoning attacks and adjust the weight proportion of in model aggregation based on evaluation score. The experiment results on three datasets illustrate that our design can reduce the training time by 30% and is robust to the representative poisoning attacks significantly, confirming the applicability of our scheme.
27
Kandati, Dasaradharami Reddy, and Thippa Reddy Gadekallu. "Genetic Clustered Federated Learning for COVID-19 Detection." Electronics 11, no. 17 (August 29, 2022): 2714. http://dx.doi.org/10.3390/electronics11172714.
Full textAbstract:
Coronavirus (COVID-19) has caused a global disaster with adverse effects on global health and the economy. Early detection of COVID-19 symptoms will help to reduce the severity of the disease. As a result, establishing a method for the initial recognition of COVID-19 is much needed. Artificial Intelligence (AI) plays a vital role in detection of COVID-19 cases. In the process of COVID-19 detection, AI requires access to patient personal records which are sensitive. The data shared can pose a threat to the privacy of patients. This necessitates a technique that can accurately detect the COVID-19 patients in a privacy preserving manner. Federated Learning (FL) is a promising solution, which can detect the COVID-19 disease at early stages without compromising the sensitive information of the patients. In this paper, we propose a novel hybrid algorithm named genetic clustered FL (Genetic CFL), that groups edge devices based on the hypertuned parameters and modifies the parameters cluster wise genetically. The experimental results proved that the proposed Genetic CFL approach performed better than conventional AI approaches.
28
Miyajima, Hirofumi, Noritaka Shigei, Hiromi Miyajima, and Norio Shiratori. "Machine Learning with Distributed Processing using Secure Divided Data: Towards Privacy-Preserving Advanced AI Processing in a Super-Smart Society." Journal of Networking and Network Applications 2, no. 1 (2022): 48–60. http://dx.doi.org/10.33969/j-nana.2022.020105.
Full textAbstract:
Towards the realization of a super-smart society, AI analysis methods that preserve the privacy of big data in cyberspace are being developed. From the viewpoint of developing machine learning as a secure and safe AI analysis method for users, many studies have been conducted in this field on 1) secure multiparty computation (SMC), 2) quasi-homomorphic encryption, and 3) federated learning, among other techniques. Previous studies have shown that both security and utility are essential for machine learning using confidential data. However, there is a trade-off between these two properties, and there are no known methods that satisfy both simultaneously at a high level. In this paper, as a superior method in both privacy-preserving of data and utility, we propose a learning method based on distributed processing using simple, secure, divided data and parameters. In this method, individual data and parameters are divided into multiple pieces using random numbers in advance, and each piece is stored in each server. The learning of the proposed method is achieved by using these data and parameters as they are divided and by repeating partial computations on each server and integrated computations at the central server. The advantages of the proposed method are the preservation of data privacy by not restoring the data and parameters during learning; the improvement of usability by realizing a machine learning method based on distributed processing, as federated learning does; and almost no degradation in accuracy compared to conventional methods. Based on the proposed method, we propose backpropagation and neural gas (NG) algorithms as examples of supervised and unsupervised machine learning applications. Our numerical simulation shows that these algorithms can achieve accuracy comparable to conventional models.
29
Nawrin Tabassum, Mustofa Ahmed, Nushrat Jahan Shorna, MD Mejbah Ur Rahman Sowad, and H M Zabir Haque. "Depression Detection Through Smartphone Sensing: A Federated Learning Approach." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 01 (January 10, 2023): 40–56. http://dx.doi.org/10.3991/ijim.v17i01.35131.
Full textAbstract:
Depression is one of the most common mental health disorders which affects thousands of lives worldwide. The variation of depressive symptoms among individuals makes it difficult to detect and diagnose early. Moreover, the diagnosing procedure relies heavily on human intervention, making it prone to mistakes. Previous research shows that smartphone sensor data correlates to the users’ mental conditions. By applying machine learning algorithms to sensor data, the mental health status of a person can be predicted. However, traditional machine learning faces privacy challenges as it involves gathering patient data for training. Newly, federated learning has emerged as an effective solution for addressing the privacy issues of classical machine learning. In this study, we apply federated learning to predict depression severity using smartphone sensing capabilities. We develop a deep neural network model and measure its performance in centralized and federated learning settings. The results are quite promising, which validates the potential of federated learning as an alternative to traditional machine learning, with the added benefit of data privacy.
30
Wei, Kang, Jun Li, Ming Ding, Chuan Ma, Howard H. Yang, Farhad Farokhi, Shi Jin, Tony Q. S. Quek, and H. Vincent Poor. "Federated Learning With Differential Privacy: Algorithms and Performance Analysis." IEEE Transactions on Information Forensics and Security 15 (2020): 3454–69. http://dx.doi.org/10.1109/tifs.2020.2988575.
Full text
31
Ho, Trang-Thi, Khoa-Dang Tran, and Yennun Huang. "FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information." Sensors 22, no. 10 (May 13, 2022): 3728. http://dx.doi.org/10.3390/s22103728.
Full textAbstract:
Coronavirus (COVID-19) has created an unprecedented global crisis because of its detrimental effect on the global economy and health. COVID-19 cases have been rapidly increasing, with no sign of stopping. As a result, test kits and accurate detection models are in short supply. Early identification of COVID-19 patients will help decrease the infection rate. Thus, developing an automatic algorithm that enables the early detection of COVID-19 is essential. Moreover, patient data are sensitive, and they must be protected to prevent malicious attackers from revealing information through model updates and reconstruction. In this study, we presented a higher privacy-preserving federated learning system for COVID-19 detection without sharing data among data owners. First, we constructed a federated learning system using chest X-ray images and symptom information. The purpose is to develop a decentralized model across multiple hospitals without sharing data. We found that adding the spatial pyramid pooling to a 2D convolutional neural network improves the accuracy of chest X-ray images. Second, we explored that the accuracy of federated learning for COVID-19 identification reduces significantly for non-independent and identically distributed (Non-IID) data. We then proposed a strategy to improve the model’s accuracy on Non-IID data by increasing the total number of clients, parallelism (client-fraction), and computation per client. Finally, for our federated learning model, we applied a differential privacy stochastic gradient descent (DP-SGD) to improve the privacy of patient data. We also proposed a strategy to maintain the robustness of federated learning to ensure the security and accuracy of the model.
32
Elkordy, Ahmed Roushdy, Jiang Zhang, Yahya H. Ezzeldin, Konstantinos Psounis, and Salman Avestimehr. "How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?" Proceedings on Privacy Enhancing Technologies 2023, no. 1 (January 2023): 510–26. http://dx.doi.org/10.56553/popets-2023-0030.
Full textAbstract:
Federated learning (FL) has attracted growing interest for enabling privacy-preserving machine learning on data stored at multiple users while avoiding moving the data off-device. However, while data never leaves users’ devices, privacy still cannot be guaranteed since significant computations on users’ training data are shared in the form of trained local models. These local models have recently been shown to pose a substantial privacy threat through different privacy attacks such as model inversion attacks. As a remedy, Secure Aggregation (SA) has been developed as a framework to preserve privacy in FL, by guaranteeing the server can only learn the global aggregated model update but not the individual model updates.While SA ensures no additional information is leaked about the individual model update beyond the aggregated model update, there are no formal guarantees on how much privacy FL with SA can actually offer; as information about the individual dataset can still potentially leak through the aggregated model computed at the server. In this work, we perform a first analysis of the formal privacy guarantees for FL with SA. Specifically, we use Mutual Information (MI) as a quantification metric and derive upper bounds on how much information about each user's dataset can leak through the aggregated model update. When using the FedSGD aggregation algorithm, our theoretical bounds show that the amount of privacy leakage reduces linearly with the number of users participating in FL with SA. To validate our theoretical bounds, we use an MI Neural Estimator to empirically evaluate the privacy leakage under different FL setups on both the MNIST and CIFAR10 datasets. Our experiments verify our theoretical bounds for FedSGD, which show a reduction in privacy leakage as the number of users and local batch size grow, and an increase in privacy leakage as the number of training rounds increases. We also observe similar dependencies for the FedAvg and FedProx protocol.
33
Chen, Xuebin, Changyin Luo, Wei Wei, Jingcheng Xu, and Shufen Zhang. "Differential Optimization Federated Incremental Learning Algorithm Based on Blockchain." Electronics 11, no. 22 (November 20, 2022): 3814. http://dx.doi.org/10.3390/electronics11223814.
Full textAbstract:
Federated learning is a hot area of concern in the field of privacy protection. There are local model parameters that are difficult to integrate, poor model timeliness, and local model training security issues. This paper proposes a blockchain-based differential optimization federated incremental learning algorithm, First, we apply differential privacy to the weighted random forest and optimize the parameters in the weighted forest to reduce the impact of adding differential privacy on the accuracy of the local model. Using different ensemble algorithms to integrate the local model parameters can improve the accuracy of the global model. At the same time, the risk of a data leakage caused by gradient update is reduced; then, incremental learning is applied to the framework of federated learning to improve the timeliness of the model; finally, the model parameters in the model training phase are uploaded to the blockchain and synchronized quickly, which reduces the cost of data storage and model parameter transmission. The experimental results show that the accuracy of the stacking ensemble model in each period is above 83.5% and the variance is lower than 10−4 for training on the public data set. The accuracy of the model has been improved, and the security and privacy of the model have been improved.
34
Agrawal, Shaashwat, Aditi Chowdhuri, Sagnik Sarkar, Ramani Selvanambi, and Thippa Reddy Gadekallu. "Temporal Weighted Averaging for Asynchronous Federated Intrusion Detection Systems." Computational Intelligence and Neuroscience 2021 (December 17, 2021): 1–10. http://dx.doi.org/10.1155/2021/5844728.
Full textAbstract:
Federated learning (FL) is an emerging subdomain of machine learning (ML) in a distributed and heterogeneous setup. It provides efficient training architecture, sufficient data, and privacy-preserving communication for boosting the performance and feasibility of ML algorithms. In this environment, the resultant global model produced by averaging various trained client models is vital. During each round of FL, model parameters are transferred from each client device to the server while the server waits for all models before it can average them. In a realistic scenario, waiting for all clients to communicate their model parameters, where client models are trained on low-power Internet of Things (IoT) devices, can result in a deadlock. In this paper, a novel temporal model averaging algorithm is proposed for asynchronous federated learning (AFL). Our approach uses a dynamic expectation function that computes the number of client models expected in each round and a weighted averaging algorithm for continuous modification of the global model. This ensures that the federated architecture is not stuck in a deadlock all the while increasing the throughput of the server and clients. To implicate the importance of asynchronicity in cybersecurity, the proposed algorithm is tested using NSL-KDD intrusion detection system datasets. The performance accuracy of the global model is about 99.5% on the dataset, outperforming traditional FL models in anomaly detection. In terms of asynchronicity, we get an increased throughput of almost 10.17% for every 30 timesteps.
35
Yaqoob, Muhammad Mateen, Muhammad Nazir, Abdullah Yousafzai, Muhammad Amir Khan, Asad Ali Shaikh, Abeer D. Algarni, and Hela Elmannai. "Modified Artificial Bee Colony Based Feature Optimized Federated Learning for Heart Disease Diagnosis in Healthcare." Applied Sciences 12, no. 23 (November 25, 2022): 12080. http://dx.doi.org/10.3390/app122312080.
Full textAbstract:
Heart disease is one of the lethal diseases causing millions of fatalities every year. The Internet of Medical Things (IoMT) based healthcare effectively enables a reduction in death rate by early diagnosis and detection of disease. The biomedical data collected using IoMT contains personalized information about the patient and this data has serious privacy concerns. To overcome data privacy issues, several data protection laws are proposed internationally. These privacy laws created a huge problem for techniques used in traditional machine learning. We propose a framework based on federated matched averaging with a modified Artificial Bee Colony (M-ABC) optimization algorithm to overcome privacy issues and to improve the diagnosis method for the prediction of heart disease in this paper. The proposed technique improves the prediction accuracy, classification error, and communication efficiency as compared to the state-of-the-art federated learning algorithms on the real-world heart disease dataset.
36
Zhang, Qingsong, Bin Gu, Cheng Deng, and Heng Huang. "Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10896–904. http://dx.doi.org/10.1609/aaai.v35i12.17301.
Full textAbstract:
Vertical federated learning (VFL) attracts increasing attention due to the emerging demands of multi-party collaborative modeling and concerns of privacy leakage. In the real VFL applications, usually only one or partial parties hold labels, which makes it challenging for all parties to collaboratively learn the model without privacy leakage. Meanwhile, most existing VFL algorithms are trapped in the synchronous computations, which leads to inefficiency in their real-world applications. To address these challenging problems, we propose a novel VFL framework integrated with new backward updating mechanism and bilevel asynchronous parallel architecture (VFB^2), under which three new algorithms, including VFB^2-SGD, -SVRG, and -SAGA, are proposed. We derive the theoretical results of the convergence rates of these three algorithms under both strongly convex and nonconvex conditions. We also prove the security of VFB^2 under semi-honest threat models. Extensive experiments on benchmark datasets demonstrate that our algorithms are efficient, scalable, and lossless.
37
Hou, Ruiqi, Fei Tang, Shikai Liang, and Guowei Ling. "Multi-Party Verifiable Privacy-Preserving Federated k-Means Clustering in Outsourced Environment." Security and Communication Networks 2021 (December 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/3630312.
Full textAbstract:
As a commonly used algorithm in data mining, clustering has been widely applied in many fields, such as machine learning, information retrieval, and pattern recognition. In reality, data to be analyzed are often distributed to multiple parties. Moreover, the rapidly increasing data volume puts heavy computing pressure on data owners. Thus, data owners tend to outsource their own data to cloud servers and obtain data analysis results for the federated data. However, the existing privacy-preserving outsourced k -means schemes cannot verify whether participants share consistent data. Considering the scenarios with multiple data owners and sensitive information security in an outsourced environment, we propose a verifiable privacy-preserving federated k -means clustering scheme. In this article, cloud servers and participants perform k -means clustering algorithm over encrypted data without exposing private data and intermediate results in each iteration. In particular, our scheme can verify the shares from participants when updating the cluster centers based on secret sharing, hash function and blockchain, so that our scheme can resist inconsistent share attacks by malicious participants. Finally, the security and experimental analysis are carried out to show that our scheme can protect private data and get high-accuracy clustering results.
38
Xu, Bin, Sheng Yan, Shuai Li, and Yidi Du. "A Federated Transfer Learning Framework Based on Heterogeneous Domain Adaptation for Students’ Grades Classification." Applied Sciences 12, no. 21 (October 22, 2022): 10711. http://dx.doi.org/10.3390/app122110711.
Full textAbstract:
In the field of educational data mining, the classification of students’ grades is a subject that receives widespread attention. However, solving this problem based on machine learning algorithms and deep learning algorithms is usually limited by large datasets. The privacy problem of educational data platforms also limits the possibility of building an extensive dataset of students’ information and behavior by gathering small datasets and then carrying out the federated training model. Therefore, the balance of educational data and the inconsistency of feature distribution are the critical problems that need to be solved urgently in educational data mining. Federated learning technology enables multiple participants to continue machine learning and deep learning in protecting data privacy and meeting legal compliance requirements to solve the data island problem. However, these methods are only applicable to the data environment with common characteristics or common samples under the alliance. This results in domain transfer between nodes. Therefore, in this paper, we propose a framework based on federated transfer learning for student classification with privacy protection. This framework introduces the domain adaptation method and extends the domain adaptation to the constraint of federated learning. Through the feature extractor, this method matches the feature distribution of each party in the feature space. Then, labels and domains are classified on each side, the model is trained, and the target model is updated by gradient aggregation. The federated learning framework based on this method can effectively solve the federated transfer learning on heterogeneous datasets. We evaluated the performance of the proposed framework for student classification on the datasets of two courses. We simulated four scenarios according to different situations in reality. Then, the results of only source domain training, only target domain training, and federated migration training are compared. The experimental results show that the heterogeneous federated transfer framework based on domain adaptation can solve federated learning and knowledge transfer problems when there are little data at the data source and can be used for students’ grades classification in small datasets.
39
Li, Zhong, Xianke Wu, and Changjun Jiang. "Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems." Security and Safety 1 (2022): 2022003. http://dx.doi.org/10.1051/sands/2022003.
Full textAbstract:
Nowadays, large numbers of smart sensors (e.g., road-side cameras) which communicate with nearby base stations could launch distributed denial of services (DDoS) attack storms in intelligent transportation systems. DDoS attacks disable the services provided by base stations. Thus in this paper, considering the uneven communication traffic flows and privacy preserving, we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the future. However, in the federated learning, we need to consider the problem of poisoning attacks due to malicious participants. The poisoning attacks will lead to the intelligent transportation systems paralysis without security protection. Traditional poisoning attacks mainly apply to the classification model with labeled data. In this paper, we propose a reinforcement learning-based poisoning method specifically for poisoning the prediction model with unlabeled data. Besides, previous related defense strategies rely on validation datasets with labeled data in the server. However, it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving, and our datasets are also unlabeled. Furthermore, we give a validation dataset-free defense strategy based on Dempster–Shafer (D–S) evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS prediction. In our experiments, we simulate 3000 points in combination with DARPA2000 dataset to carry out evaluations. The results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short time. Meanwhile, we compare our proposed defense algorithm with three popularly used defense algorithms. The results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability.
40
Wang, Shuyi, and Longxiang Yang. "Securing Dynamic Service Function Chain Orchestration in EC-IoT Using Federated Learning." Sensors 22, no. 23 (November 22, 2022): 9041. http://dx.doi.org/10.3390/s22239041.
Full textAbstract:
Dynamic service orchestration is becoming more and more necessary as IoT and edge computing technologies continue to advance due to the flexibility and diversity of services. With the surge in the number of edge devices and the increase in data volume of IoT scenarios, there are higher requirements for the transmission security of privacy information from each edge device and the processing efficiency of SFC orchestration. This paper proposes a kind of dynamic SFC orchestration security algorithm applicable to EC-IoT scenarios based on the federated learning framework, combined with a block coordinated descent approach and the quadratic penalty algorithm to achieve communication efficiency and data privacy protection. A deep reinforcement learning algorithm is used to simultaneously adapt the SFC orchestration method in order to dynamically observe environmental changes and decrease end-to-end delay. The experimental results show that compared with the existing dynamic SFC orchestration algorithms, the proposed algorithm can achieve better convergence and latency performance under the condition of privacy protection; the overall latency is reduced by about 33%, and the overall convergence speed is improved by about 9%, which not only achieves the security of data privacy protection of edge computing nodes, but also meets the requirements of dynamic SFC orchestration.
41
Lee, Haeyun, Young Jun Chai, Hyunjin Joo, Kyungsu Lee, Jae Youn Hwang, Seok-Mo Kim, Kwangsoon Kim, et al. "Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in a Real Health Care Environment." JMIR Medical Informatics 9, no. 5 (May 18, 2021): e25869. http://dx.doi.org/10.2196/25869.
Full textAbstract:
Background Federated learning is a decentralized approach to machine learning; it is a training strategy that overcomes medical data privacy regulations and generalizes deep learning algorithms. Federated learning mitigates many systemic privacy risks by sharing only the model and parameters for training, without the need to export existing medical data sets. In this study, we performed ultrasound image analysis using federated learning to predict whether thyroid nodules were benign or malignant. Objective The goal of this study was to evaluate whether the performance of federated learning was comparable with that of conventional deep learning. Methods A total of 8457 (5375 malignant, 3082 benign) ultrasound images were collected from 6 institutions and used for federated learning and conventional deep learning. Five deep learning networks (VGG19, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) were used. Using stratified random sampling, we selected 20% (1075 malignant, 616 benign) of the total images for internal validation. For external validation, we used 100 ultrasound images (50 malignant, 50 benign) from another institution. Results For internal validation, the area under the receiver operating characteristic (AUROC) curve for federated learning was between 78.88% and 87.56%, and the AUROC for conventional deep learning was between 82.61% and 91.57%. For external validation, the AUROC for federated learning was between 75.20% and 86.72%, and the AUROC curve for conventional deep learning was between 73.04% and 91.04%. Conclusions We demonstrated that the performance of federated learning using decentralized data was comparable to that of conventional deep learning using pooled data. Federated learning might be potentially useful for analyzing medical images while protecting patients’ personal information.
42
Benedict, Shajulin, Deepumon Saji, Rajesh P. Sukumaran, and Bhagyalakshmi M. "Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications." September 2021 3, no. 3 (August 30, 2021): 196–217. http://dx.doi.org/10.36548/jaicn.2021.3.004.
Full textAbstract:
The biggest realization of the Machine Learning (ML) in societal applications, including air quality prediction, has been the inclusion of novel learning techniques with the focus on solving privacy and scalability issues which capture the inventiveness of tens of thousands of data scientists. Transferring learning models across multi-regions or locations has been a considerable challenge as sufficient technologies were not adopted in the recent past. This paper proposes a Blockchain- enabled Federated Learning Air Quality Prediction (BFL-AQP) framework on Kubernetes cluster which transfers the learning model parameters of ML algorithms across distributed cluster nodes and predicts the air quality parameters of different locations. Experiments were carried out to explore the frame- work and transfer learning models of air quality prediction parameters. Besides, the performance aspects of increasing the Kubernetes cluster nodes of blockchains in the federated learning environment were studied; the time taken to establish seven blockchain organizations on top of the Kubernetes cluster while investigating into the federated learning algorithms namely Federated Random Forests (FRF) and Federated Linear Regression (FLR) for air quality predictions, were revealed in the paper.
43
Bemani, Ali, and Niclas Björsell. "Aggregation Strategy on Federated Machine Learning Algorithm for Collaborative Predictive Maintenance." Sensors 22, no. 16 (August 19, 2022): 6252. http://dx.doi.org/10.3390/s22166252.
Full textAbstract:
Industry 4.0 lets the industry build compact, precise, and connected assets and also has made modern industrial assets a massive source of data that can be used in process optimization, defining product quality, and predictive maintenance (PM). Large amounts of data are collected from machines, processed, and analyzed by different machine learning (ML) algorithms to achieve effective PM. These machines, assumed as edge devices, transmit their data readings to the cloud for processing and modeling. Transmitting massive amounts of data between edge and cloud is costly, increases latency, and causes privacy concerns. To address this issue, efforts have been made to use edge computing in PM applications., reducing data transmission costs and increasing processing speed. Federated learning (FL) has been proposed a mechanism that provides the ability to create a model from distributed data in edge, fog, and cloud layers without violating privacy and offers new opportunities for a collaborative approach to PM applications. However, FL has challenges in confronting with asset management in the industry, especially in the PM applications, which need to be considered in order to be fully compatible with these applications. This study describes distributed ML for PM applications and proposes two federated algorithms: Federated support vector machine (FedSVM) with memory for anomaly detection and federated long-short term memory (FedLSTM) for remaining useful life (RUL) estimation that enables factories at the fog level to maximize their PM models’ accuracy without compromising their privacy. A global model at the cloud level has also been generated based on these algorithms. We have evaluated the approach using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset to predict engines’ RUL Experimental results demonstrate the advantage of FedSVM and FedLSTM in terms of model accuracy, model convergence time, and network usage resources.
44
Park, Sunghwan, Yeryoung Suh, and Jaewoo Lee. "FedPSO: Federated Learning Using Particle Swarm Optimization to Reduce Communication Costs." Sensors 21, no. 2 (January 16, 2021): 600. http://dx.doi.org/10.3390/s21020600.
Full textAbstract:
Federated learning is a learning method that collects only learned models on a server to ensure data privacy. This method does not collect data on the server but instead proceeds with data directly from distributed clients. Because federated learning clients often have limited communication bandwidth, communication between servers and clients should be optimized to improve performance. Federated learning clients often use Wi-Fi and have to communicate in unstable network environments. However, as existing federated learning aggregation algorithms transmit and receive a large amount of weights, accuracy is significantly reduced in unstable network environments. In this study, we propose the algorithm using particle swarm optimization algorithm instead of FedAvg, which updates the global model by collecting weights of learned models that were mainly used in federated learning. The algorithm is named as federated particle swarm optimization (FedPSO), and we increase its robustness in unstable network environments by transmitting score values rather than large weights. Thus, we propose a FedPSO, a global model update algorithm with improved network communication performance, by changing the form of the data that clients transmit to servers. This study showed that applying FedPSO significantly reduced the amount of data used in network communication and improved the accuracy of the global model by an average of 9.47%. Moreover, it showed an improvement in loss of accuracy by approximately 4% in experiments on an unstable network.
45
Xuan, Shichang, Ming Jin, Xin Li, Zhaoyuan Yao, Wu Yang, and Dapeng Man. "DAM-SE: A Blockchain-Based Optimized Solution for the Counterattacks in the Internet of Federated Learning Systems." Security and Communication Networks 2021 (July 1, 2021): 1–14. http://dx.doi.org/10.1155/2021/9965157.
Full textAbstract:
The rapid development in network technology has resulted in the proliferation of Internet of Things (IoT). This trend has led to a widespread utilization of decentralized data and distributed computing power. While machine learning can benefit from the massive amount of IoT data, privacy concerns and communication costs have caused data silos. Although the adoption of blockchain and federated learning technologies addresses the security issues related to collusion attacks and privacy leakage in data sharing, the “free-rider attacks” and “model poisoning attacks” in the federated learning process require auditing of the training models one by one. However, that increases the communication cost of the entire training process. Hence, to address the problem of increased communication cost due to node security verification in the blockchain-based federated learning process, we propose a communication cost optimization method based on security evaluation. By studying the verification mechanism for useless or malicious nodes, we also introduce a double-layer aggregation model into the federated learning process by combining the competing voting verification methods and aggregation algorithms. The experimental comparisons verify that the proposed model effectively reduces the communication cost of the node security verification in the blockchain-based federated learning process.
46
Xu, Gang, De-Lun Kong, Xiu-Bo Chen, and Xin Liu. "Lazy Aggregation for Heterogeneous Federated Learning." Applied Sciences 12, no. 17 (August 25, 2022): 8515. http://dx.doi.org/10.3390/app12178515.
Full textAbstract:
Federated learning (FL) is a distributed neural network training paradigm with privacy protection. With the premise of ensuring that local data are not leaked, the multi-device cooperation trains the model and improves its normalization. Unlike centralized training, FL is susceptible to heterogeneous data, biased gradient estimations hinder the convergence of the global model, and traditional sampling techniques cannot apply FL due to privacy constraints. Therefore, this paper proposes a novel FL framework, federated lazy aggregation (FedLA), which reduces aggregation frequency to obtain high-quality gradients and improve robustness in non-IID. To judge the aggregating timings, the change rate of the models’ weight divergence (WDR) is introduced to FL. Furthermore, the collected gradients also facilitate FL walking out of the saddle point without extra communications. The cross-device momentum (CDM) mechanism could significantly improve the upper limit performance of the global model in non-IID. We evaluate the performance of several popular algorithms, including FedLA and FedLA with momentum (FedLAM). The results show that FedLAM achieves the best performance in most scenarios and the performance of FL can also be improved in IID scenarios.
47
Ou, Wei, Jianhuan Zeng, Zijun Guo, Wanqin Yan, Dingwan Liu, and Stelios Fuentes. "A homomorphic-encryption-based vertical federated learning scheme for rick management." Computer Science and Information Systems 17, no. 3 (2020): 819–34. http://dx.doi.org/10.2298/csis190923022o.
Full textAbstract:
With continuous improvements of computing power, great progresses in algorithms and massive growth of data, artificial intelligence technologies have entered the third rapid development era. However, With the great improvements in artificial intelligence and the arrival of the era of big data, contradictions between data sharing and user data privacy have become increasingly prominent. Federated learning is a technology that can ensure the user privacy and train a better model from different data providers. In this paper, we design a vertical federated learning system for the for Bayesian machine learning with the homomorphic encryption. During the training progress, raw data are leaving locally, and encrypted model information is exchanged. The model trained by this system is comparable (up to 90%) to those models trained by a single union server under the consideration of privacy. This system can be widely used in risk control, medical, financial, education and other fields. It is of great significance to solve data islands problem and protect users? privacy.
48
HUANG, Fang, Zhijun FANG, Zhicai SHI, Lehui ZHUANG, Xingchen LI, and Bo HUANG. "A Federated Domain Adaptation Algorithm Based on Knowledge Distillation and Contrastive Learning." Wuhan University Journal of Natural Sciences 27, no. 6 (December 2022): 499–507. http://dx.doi.org/10.1051/wujns/2022276499.
Full textAbstract:
Smart manufacturing suffers from the heterogeneity of local data distribution across parties, mutual information silos and lack of privacy protection in the process of industry chain collaboration. To address these problems, we propose a federated domain adaptation algorithm based on knowledge distillation and contrastive learning. Knowledge distillation is used to extract transferable integration knowledge from the different source domains and the quality of the extracted integration knowledge is used to assign reasonable weights to each source domain. A more rational weighted average aggregation is used in the aggregation phase of the center server to optimize the global model, while the local model of the source domain is trained with the help of contrastive learning to constrain the local model optimum towards the global model optimum, mitigating the inherent heterogeneity between local data. Our experiments are conducted on the largest domain adaptation dataset, and the results show that compared with other traditional federated domain adaptation algorithms, the algorithm we proposed trains a more accurate model, requires fewer communication rounds, makes more effective use of imbalanced data in the industrial area, and protects data privacy.
49
Tao, Jiang, Zhen Gao, and Zhaohui Guo. "Training Vision Transformers in Federated Learning with Limited Edge-Device Resources." Electronics 11, no. 17 (August 23, 2022): 2638. http://dx.doi.org/10.3390/electronics11172638.
Full textAbstract:
Vision transformers (ViTs) demonstrate exceptional performance in numerous computer vision tasks owing to their self-attention modules. Despite improved network performance, transformers frequently require significant computational resources. The increasing need for data privacy has encouraged the development of federated learning (FL). Traditional FL places a computing burden on edge devices. However, ViTs cannot be directly applied through FL on resource-constrained edge devices. To utilize the powerful ViT structure, we reformulated FL as a federated knowledge distillation training algorithm called FedVKD. FedVKD uses an alternating minimization strategy to train small convolutional neural networks on edge nodes and periodically transfers their knowledge to a large server-side transformer encoder via knowledge distillation. FedVKD affords the benefits of reduced edge-computing load and improved performance for vision tasks, while preserving FedGKT-like asynchronous training. We used four datasets and their non-IID variations to test the proposed FedVKD. When utilizing a larger dataset, FedVKD achieved higher accuracy than FedGKT and FedAvg.
50
Shamim, Rejuwan, Md Arshad, and Dr Vinay Pandey. "A Machine Learning Model to Protect Privacy Using Federal Learning with Homomorphy Encryption." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (October 31, 2022): 989–94. http://dx.doi.org/10.22214/ijraset.2022.47120.
Full textAbstract:
Abstract: Machine learning technologies have a marvelous success in emancipating real-world Artificial Intelligence applications. But still, vast numbers of sensitive data are produced every second time in different forms. This data can be in the form of health records, shopping records, internet searching records, mobile and laptop activities, and so on. This data can be used to train our Machine learning /Deep learning models to make Artificial intelligence-based technologies better than their previous generation. However, in today’s world, one of the significant challenges that need to be a concern in machine learning is regarding data breaches while training the model. Since federated learning trains machine learning algorithms in various devices or servers without sharing sample data. This paper discusses the framework of federated learning and homomorphic encryption and how both frameworks work together so that the outcoming data will be more precious and accurate without bothering data breaches. Later, we focus on its futuristic applications in various fields to improve technology.