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1
Шубин, Б., Т. Максимюк, О. Яремко, Л. Фабрі та Д. Мрозек. "МОДЕЛЬ ІНТЕГРАЦІЇ ФЕДЕРАТИВНОГО НАВЧАННЯ В МЕРЕЖІ МОБІЛЬНОГО ЗВ’ЯЗКУ 5-ГО ПОКОЛІННЯ". Information and communication technologies, electronic engineering 2, № 1 (серпень 2022): 26–35. http://dx.doi.org/10.23939/ictee2022.01.026.
Повний текст джерелаАнотація:
This paper investigates the main advantages of using Federated Learning (FL) for sharing experiences between intelligent devices in the environment of 5th generation mobile communication networks. This approach makes it possible to build effective machine learning algorithms using confidential data, the loss of which may be undesirable or even dangerous for users. Therefore, for the tasks where the confidentiality of the data is required for processing and analysis, we suggest using Federated Learning (FL) approaches. In this case, all users' personal information will be processed locally on their devices. FL ensures the security of confidential data for subscribers, allows mobile network operators to reduce the amount of redundant information in the radio channel, and also allows optimizing the functioning of the mobile network. The paper presents a three-level model of integration of Federated Learning into the mobile network and describes the main features of this approach, as well as experimental studies that demonstrate the results of the proposed approach.
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Zhang, Kainan, Zhipeng Cai, and Daehee Seo. "Privacy-Preserving Federated Graph Neural Network Learning on Non-IID Graph Data." Wireless Communications and Mobile Computing 2023 (February 3, 2023): 1–13. http://dx.doi.org/10.1155/2023/8545101.
Повний текст джерелаАнотація:
Since the concept of federated learning (FL) was proposed by Google in 2017, many applications have been combined with FL technology due to its outstanding performance in data integration, computing performance, privacy protection, etc. However, most traditional federated learning-based applications focus on image processing and natural language processing with few achievements in graph neural networks due to the graph’s nonindependent identically distributed (IID) nature. Representation learning on graph-structured data generates graph embedding, which helps machines understand graphs effectively. Meanwhile, privacy protection plays a more meaningful role in analyzing graph-structured data such as social networks. Hence, this paper proposes PPFL-GNN, a novel privacy-preserving federated graph neural network framework for node representation learning, which is a pioneer work for graph neural network-based federated learning. In PPFL-GNN, clients utilize a local graph dataset to generate graph embeddings and integrate information from other collaborative clients to utilize federated learning to produce more accurate representation results. More importantly, by integrating embedding alignment techniques in PPFL-GNN, we overcome the obstacles of federated learning on non-IID graph data and can further reduce privacy exposure by sharing preferred information.
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Hang, Yifei. "Federated learning-based neural network for hotel cancellation prediction." Applied and Computational Engineering 45, no. 1 (March 15, 2024): 190–95. http://dx.doi.org/10.54254/2755-2721/45/20241092.
Повний текст джерелаАнотація:
Hotel reservations have become a prevalent choice for customers. However, cancellations of these reservations present a significant challenge for hotels, potentially resulting in financial losses and a decline in customer satisfaction. To address the issue of improper management of cancellations and minimize losses, machine learning can be employed to analyze and predict cancellations based on customer information. In cooperative scenarios where hotels collaborate to train a unified model, traditional algorithms that aggregate all data raise concerns about the protection of sensitive customer information. In this context, federated learning emerges as an effective solution to ensure the privacy protection of customers while achieving the desired predictive outcomes. Thus, to protect customer privacy while preserving performance of the federated learning model in comparison to the non-federated version, this work proposed to implement both federated learning and non-federated algorithms based on neural network to make predictions of cancellation based on multiple factors. The federated learning approach achieved a final testing accuracy of 76.64%. Although this accuracy was about 9% lower than the non-federated case, its loss was over half the loss of non-federated, and its testing accuracy was similar to the training accuracy, while the non-federated algorithms testing accuracy was approximately 4% lower than the training one. Such results indicate that although accuracy was relatively lower, the federated learning approach prevented the overfitting problem in the non-federated case, while the data privacy problem was resolved.
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Yu, Yun William, and Griffin M. Weber. "Balancing Accuracy and Privacy in Federated Queries of Clinical Data Repositories: Algorithm Development and Validation." Journal of Medical Internet Research 22, no. 11 (November 3, 2020): e18735. http://dx.doi.org/10.2196/18735.
Повний текст джерелаАнотація:
Background Over the past decade, the emergence of several large federated clinical data networks has enabled researchers to access data on millions of patients at dozens of health care organizations. Typically, queries are broadcast to each of the sites in the network, which then return aggregate counts of the number of matching patients. However, because patients can receive care from multiple sites in the network, simply adding the numbers frequently double counts patients. Various methods such as the use of trusted third parties or secure multiparty computation have been proposed to link patient records across sites. However, they either have large trade-offs in accuracy and privacy or are not scalable to large networks. Objective This study aims to enable accurate estimates of the number of patients matching a federated query while providing strong guarantees on the amount of protected medical information revealed. Methods We introduce a novel probabilistic approach to running federated network queries. It combines an algorithm called HyperLogLog with obfuscation in the form of hashing, masking, and homomorphic encryption. It is tunable, in that it allows networks to balance accuracy versus privacy, and it is computationally efficient even for large networks. We built a user-friendly free open-source benchmarking platform to simulate federated queries in large hospital networks. Using this platform, we compare the accuracy, k-anonymity privacy risk (with k=10), and computational runtime of our algorithm with several existing techniques. Results In simulated queries matching 1 to 100 million patients in a 100-hospital network, our method was significantly more accurate than adding aggregate counts while maintaining k-anonymity. On average, it required a total of 12 kilobytes of data to be sent to the network hub and added only 5 milliseconds to the overall federated query runtime. This was orders of magnitude better than other approaches, which guaranteed the exact answer. Conclusions Using our method, it is possible to run highly accurate federated queries of clinical data repositories that both protect patient privacy and scale to large networks.
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Kostenko, Valery Alekseevich, and Alisa Evgenievna Selezneva. "Types of Attacks on Federated Neural Networks and Methods of Protection." Proceedings of the Institute for System Programming of the RAS 36, no. 1 (2024): 35–44. http://dx.doi.org/10.15514/ispras-2024-36(1)-3.
Повний текст джерелаАнотація:
Federated learning is a technology for privacy-preserving learning in distributed storage systems. This training allows you to create a general forecasting model, storing all the data in your storage systems. Several devices take part in training the general model, and each device has its own unique data on which the neural network is trained. The interaction of devices occurs only to adjust the weights of the general model. After which, the updated model is transmitted to all devices. Training on multiple devices creates many attack opportunities against this type of network. After training on a local device, model data is sent via some type of communication to a central server or global model. Therefore, vulnerabilities in a federated network are possible not only at the training stage on a separate device, but also at the data exchange stage. All this together increases the number of possible vulnerabilities of federated neural networks. As is known, not only neural networks, but also other models can be used to build federated classifiers. Therefore, the types of attacks directly on the network also depend on the type of model used. Federated neural networks are a rather complex design, different from neural networks and other classifiers, which can be vulnerable to various types of attacks because training occurs on different devices, and both neural networks and simpler algorithms can be used. In addition, it is necessary to ensure data transfer between devices. All attacks come down to several main types that exploit classifier vulnerabilities. It is possible to implement protection against attacks by improving the architecture of the classifier itself and paying attention to data encryption.
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Ma, Xiaoyu, and Lize Gu. "Research and Application of Generative-Adversarial-Network Attacks Defense Method Based on Federated Learning." Electronics 12, no. 4 (February 15, 2023): 975. http://dx.doi.org/10.3390/electronics12040975.
Повний текст джерелаАнотація:
In recent years, Federated Learning has attracted much attention because it solves the problem of data silos in machine learning to a certain extent. However, many studies have shown that attacks based on Generative Adversarial Networks pose a great threat to Federated Learning. This paper proposes Defense-GAN, a defense method against Generative Adversarial Network attacks under Federated Learning. Under this method, the attacker cannot learn the real image data distribution. Each Federated Learning participant uses SHAP to explain the model and masks the pixel features that have a greater impact on classification and recognition in their respective image data. The experimental results show that while attacking the federated training model using masked images, the attacker cannot always obtain the ground truth of the images. At the same time, this paper also uses CutMix to improve the generalization ability of the model, and the obtained model accuracy is only 1% different from that of the model trained with the original data. The results show that the defense method proposed in this paper can not only resist Generative Adversarial Network attacks in Federated Learning and protect client privacy, but also ensure that the model accuracy of the Federated model will not be greatly affected.
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Tian, Mengmeng. "An Contract Theory based Federated Learning Aggregation Algorithm in IoT Network." Journal of Physics: Conference Series 2258, no. 1 (April 1, 2022): 012008. http://dx.doi.org/10.1088/1742-6596/2258/1/012008.
Повний текст джерелаАнотація:
Abstract Taking IoT devices as the edge nodes is one of the powerful way to offloading the federated task since IoT devices are closer to the data generation end. The aggregation efficiency of federated learning in the IoT environment is inefficiency since the server of federated learning can not know the data quality of heterogeneous IoT device. How to encourage IoT edge clients to participate in federated learning and maximize the aggregation effect of the global model is an important problem. This paper proposes a federated learning aggregation model based on contract theory incentive mechanism. Our experimental results show that the proposed algorithm effectively improves the aggregation efficiency of federated learning compared with FedAvg algorithm.
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Al-Tameemi, M., M. B. Hassan, and S. A. Abass. "Federated Learning (FL) – Overview." LETI Transactions on Electrical Engineering & Computer Science 17, no. 5 (2024): 74–82. http://dx.doi.org/10.32603/2071-8985-2024-17-5-74-82.
Повний текст джерелаАнотація:
Explores the fundamental aspects of federated learning (FL) in the context of intrusion detection systems (IDS) within Internet of Things (IoT) networks. Federated learning presents an innovative approach to training machine learning models on distributed devices, thereby minimizing the need to transmit sensitive data to central servers. We classify FL into horizontal, vertical, and federated transfer learning and examine their application in IDS systems. Additionally, we analyze the network structure of FL, encompassing centralized and decentralized FL. Based on the conducted review, it can be concluded that FL holds promise for enhancing data privacy and anomaly detection efficiency in IoT networks.
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Rizzato, Matteo, Youssef Laarouchi, and Christophe Geissler. "Using Federated Learning for Collaborative Intrusion Detection Systems." Journal of Systemics, Cybernetics and Informatics 21, no. 3 (June 2023): 29–36. http://dx.doi.org/10.54808/jsci.21.03.29.
Повний текст джерелаАнотація:
Neural networks have become cutting edge machine learning models for detecting network attacks. Traditional implementations provide fast and accurate predictions, but require centralised storage of labelled historical data for training. This solution is not always suitable for real-world applications, where regulatory constraints and privacy concerns hamper the collection of sensitive data into a single server. Federated Learning has recently been proposed as a framework for training a centralised model without the need to share data between different providers. We use the CICIDS2017 dataset provided by the Canadian Institute of Cybersecurity to demonstrate the benefits of Neural Networks-based Federated Learning for the detection of the most relevant types of network attacks. We conclude that a federated-trained neural network outperforms locally-trained models (at isoarchitecture) in terms of F1-score and False Negative detection ratio. Further, such model has a minor loss of performance and convergence rapidity compared to a model trained over a hypothetical centralised dataset.
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Wang, Shuangzhong, and Ying Zhang. "Multi-Level Federated Network Based on Interpretable Indicators for Ship Rolling Bearing Fault Diagnosis." Journal of Marine Science and Engineering 10, no. 6 (May 28, 2022): 743. http://dx.doi.org/10.3390/jmse10060743.
Повний текст джерелаАнотація:
The federated learning network requires all the connection weights to be shared among the server and clients during training which increases the risk of data leakage. Meanwhile, the traditional federated learning method has a poor diagnostic effect for non-independently identically distributed data. In order to address these issues, a multi-level federated network based on interpretable indicators was proposed in this manuscript. Firstly, an interpretable adaptive sparse deep network is constructed based on the interpretability principle. Secondly, the relevance map of the network is constructed based on interpretable indicators. Based on this map, the contribution of the connection weights in the network is used to build a multi-level federated network. Finally, the effectiveness of the proposed algorithm has been proved through experimental validation in the paper.
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Meeker, Daniella, Xiaoqian Jiang, Michael E. Matheny, Claudiu Farcas, Michel D’Arcy, Laura Pearlman, Lavanya Nookala, et al. "A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research." Journal of the American Medical Informatics Association 22, no. 6 (July 3, 2015): 1187–95. http://dx.doi.org/10.1093/jamia/ocv017.
Повний текст джерелаАнотація:
Abstract Background Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. Objective The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. Materials and Methods Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. Results The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. Discussion and Conclusion Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks.
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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.
Повний текст джерелаАнотація:
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.
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Luo, Yihang, Bei Gong, Haotian Zhu, and Chong Guo. "A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security." Applied Sciences 13, no. 19 (September 22, 2023): 10586. http://dx.doi.org/10.3390/app131910586.
Повний текст джерелаАнотація:
The machine learning paradigms driven by the sixth-generation network (6G) facilitate an ultra-fast and low-latency communication environment. However, specific research and practical applications have revealed that there are still various issues regarding their applicability. A system named Incentivizing Secure Federated Learning Systems (ISFL-Sys) is proposed, consisting of a blockchain module and a federated learning module. A data-security-oriented trustworthy federated learning mechanism called Efficient Trustworthy Federated Learning (ETFL) is introduced in the system. Utilizing a directed acyclic graph as the ledger for edge nodes, an incentive mechanism has been devised through the use of smart contracts to encourage the involvement of edge nodes in federated learning. Experimental simulations have demonstrated the efficient security of the proposed federated learning mechanism. Furthermore, compared to benchmark algorithms, the mechanism showcases improved convergence and accuracy.
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Naeem, Muhammad Ali, Yahui Meng, and Sushank Chaudhary. "The Impact of Federated Learning on Improving the IoT-Based Network in a Sustainable Smart Cities." Electronics 13, no. 18 (September 13, 2024): 3653. http://dx.doi.org/10.3390/electronics13183653.
Повний текст джерелаАнотація:
The caching mechanism of federated learning in smart cities is vital for improving data handling and communication in IoT environments. Because it facilitates learning among separately connected devices, federated learning makes it possible to quickly update caching strategies in response to data usage without invading users’ privacy. Federated learning caching promotes improved dynamism, effectiveness, and data reachability for smart city services to function properly. In this paper, a new caching strategy for Named Data Networking (NDN) based on federated learning in smart cities’ IoT contexts is proposed and described. The proposed strategy seeks to apply a federated learning technique to improve content caching more effectively based on its popularity, thereby improving its performance on the network. The proposed strategy was compared to the benchmark in terms of the cache hit ratio, delay in content retrieval, and energy utilization. These benchmarks evidence that the suggested caching strategy performs far better than its counterparts in terms of cache hit rates, the time taken to fetch the content, and energy consumption. These enhancements result in smarter and more efficient smart city networks, a clear indication of how federated learning can revolutionize content caching in NDN-based IoT.
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Calo, James, and Benny Lo. "Federated Blockchain Learning at the Edge." Information 14, no. 6 (May 30, 2023): 318. http://dx.doi.org/10.3390/info14060318.
Повний текст джерелаАнотація:
Machine learning, particularly using neural networks, is now widely adopted in practice even with the IoT paradigm; however, training neural networks at the edge, on IoT devices, remains elusive, mainly due to computational requirements. Furthermore, effective training requires large quantities of data and privacy concerns restrict accessible data. Therefore, in this paper, we propose a method leveraging a blockchain and federated learning to train neural networks at the edge effectively bypassing these issues and providing additional benefits such as distributing training across multiple devices. Federated learning trains networks without storing any data and aggregates multiple networks, trained on unique data, forming a global network via a centralized server. By leveraging the decentralized nature of a blockchain, this centralized server is replaced by a P2P network, removing the need for a trusted centralized server and enabling the learning process to be distributed across participating devices. Our results show that networks trained in such a manner have negligible differences in accuracy compared to traditionally trained networks on IoT devices and are less prone to overfitting. We conclude that not only is this a viable alternative to traditional paradigms but is an improvement that contains a wealth of benefits in an ecosystem such as a hospital.
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Liu, Zhetong, Qiugang Zhan, Xiurui Xie, Bingchao Wang, and Guisong Liu. "Federal SNN Distillation: A Low-Communication-Cost Federated Learning Framework for Spiking Neural Networks." Journal of Physics: Conference Series 2216, no. 1 (March 1, 2022): 012078. http://dx.doi.org/10.1088/1742-6596/2216/1/012078.
Повний текст джерелаАнотація:
Abstract In recent years, research on the federated spiking neural network (SNN) framework has attracted increasing attention in the area of on-chip learning for embedded devices, because of its advantages of low power consumption and privacy security. Most of the existing federated SNN frameworks are based on the classical federated learning framework -- Federated Average (FedAvg) framework, where internal communication is achieved by exchanging network parameters or gradients. However, although these frameworks take a series of methods to reduce the communication cost, the communication of frameworks still increases with the scale of the backbone network. To solve the problem, we propose a new federated SNN framework, Federal SNN distillation (FSD), whose communication is independent of the scale of the network. Through the idea of knowledge distillation, FSD replaces the network parameters or gradients with the output spikes of SNN, which greatly reduces the communication while ensuring the effect. In addition, we propose a lossless compression algorithm to further compress the binary output spikes of SNN. The proposed framework FSD is compared with the existing FedAvg frameworks on MNIST, Fashion MNIST and CIFAR10 datasets. The experiment results demonstrate that FSD communication is decreased by 1-2 orders of magnitude when reaching the same accuracy.
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Zou, Qianying, Yushi Li, Xinyue Jiang, Yuepeng Zan, and Fengyu Liu. "Network Intrusion Detection Based on Convolutional Recurrent Neural Network, Random Forest, and Federated Learning." Journal of Computing and Information Technology 32, no. 2 (September 30, 2024): 97–125. http://dx.doi.org/10.20532/cit.2024.1005838.
Повний текст джерелаАнотація:
This paper presents a novel network intrusion detection framework that combines convolutional recurrent neural networks (CRNN) and random forest (RF) models within a federated learning setting. The proposed approach aims to address the challenges of data privacy, computational efficiency, and model generalization in traditional network intrusion detection methods. By leveraging the spatial feature extraction capabilities of CRNN and the feature selection and noise reduction properties of RF, the framework enhances the accuracy and robustness of attack detection. The integration of federated learning enables collaborative model training without compromising data privacy. Extensive experiments on benchmark datasets demonstrate the superiority of the proposed method compared to state-of-the-art techniques, achieving high performance metrics such as accuracy, precision, recall, F1 score, and AUC. The proposed framework offers a promising solution for secure and efficient network intrusion detection in real-world scenarios, contributing to the advancement of cybersecurity practices.
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Massingham, Peter. "Australia's Federated Network Universities: What happened?" Journal of Higher Education Policy and Management 23, no. 1 (May 2001): 19–32. http://dx.doi.org/10.1080/13600800020047216.
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Estiri, Hossein, Jeffrey G. Klann, Sarah R. Weiler, Ernest Alema-Mensah, R. Joseph Applegate, Galina Lozinski, Nandan Patibandla, et al. "A federated EHR network data completeness tracking system." Journal of the American Medical Informatics Association 26, no. 7 (March 29, 2019): 637–45. http://dx.doi.org/10.1093/jamia/ocz014.
Повний текст джерелаАнотація:
Abstract Objective The study sought to design, pilot, and evaluate a federated data completeness tracking system (CTX) for assessing completeness in research data extracted from electronic health record data across the Accessible Research Commons for Health (ARCH) Clinical Data Research Network. Materials and Methods The CTX applies a systems-based approach to design workflow and technology for assessing completeness across distributed electronic health record data repositories participating in a queryable, federated network. The CTX invokes 2 positive feedback loops that utilize open source tools (DQe-c and Vue) to integrate technology and human actors in a system geared for increasing capacity and taking action. A pilot implementation of the system involved 6 ARCH partner sites between January 2017 and May 2018. Results The ARCH CTX has enabled the network to monitor and, if needed, adjust its data management processes to maintain complete datasets for secondary use. The system allows the network and its partner sites to profile data completeness both at the network and partner site levels. Interactive visualizations presenting the current state of completeness in the context of the entire network as well as changes in completeness across time were valued among the CTX user base. Discussion Distributed clinical data networks are complex systems. Top-down approaches that solely rely on technology to report data completeness may be necessary but not sufficient for improving completeness (and quality) of data in large-scale clinical data networks. Improving and maintaining complete (high-quality) data in such complex environments entails sociotechnical systems that exploit technology and empower human actors to engage in the process of high-quality data curating. Conclusions The CTX has increased the network’s capacity to rapidly identify data completeness issues and empowered ARCH partner sites to get involved in improving the completeness of respective data in their repositories.
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Karras, Aristeidis, Anastasios Giannaros, Leonidas Theodorakopoulos, George A. Krimpas, Gerasimos Kalogeratos, Christos Karras, and Spyros Sioutas. "FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE." Electronics 12, no. 22 (November 13, 2023): 4633. http://dx.doi.org/10.3390/electronics12224633.
Повний текст джерелаАнотація:
In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big Data, intricately designed to ensure privacy preservation across extensive system networks. By utilising Federated Learning (FL), Apache Spark, and Federated AI Technology Enabler (FATE), we skilfully investigated the complicated area of IoT data management while simultaneously reinforcing privacy across broad network configurations. Our FLIBD architecture was thoughtfully designed to safeguard data and model privacy through a synergistic integration of distributed model training and secure model consolidation. Notably, we delved into an in-depth examination of adversarial activities within federated learning contexts. The Federated Adversarial Attack for Multi-Task Learning (FAAMT) was thoroughly assessed, unmasking its proficiency in showcasing and exploiting vulnerabilities across various federated learning approaches. Moreover, we offer an incisive evaluation of numerous federated learning defence mechanisms, including Romoa and RFA, in the scope of the FAAMT. Utilising well-defined evaluation metrics and analytical processes, our study demonstrated a resilient framework suitable for managing IoT Big Data across widespread deployments, while concurrently presenting a solid contribution to the progression and discussion surrounding defensive methodologies within the federated learning and IoT areas.
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Liu, Fengchun, Meng Li, Xiaoxiao Liu, Tao Xue, Jing Ren, and Chunying Zhang. "A Review of Federated Meta-Learning and Its Application in Cyberspace Security." Electronics 12, no. 15 (July 31, 2023): 3295. http://dx.doi.org/10.3390/electronics12153295.
Повний текст джерелаАнотація:
In recent years, significant progress has been made in the application of federated learning (FL) in various aspects of cyberspace security, such as intrusion detection, privacy protection, and anomaly detection. However, the robustness of federated learning in the face of malicious attacks (such us adversarial attacks, backdoor attacks, and poisoning attacks) is weak, and the unfair allocation of resources leads to slow convergence and inefficient communication efficiency regarding FL models. Additionally, the scarcity of malicious samples during FL model training and the heterogeneity of data result in a lack of personalization in FL models. These challenges pose significant obstacles to the application of federated learning in the field of cyberspace security. To address these issues, the introduction of meta-learning into federated learning has been proposed, resulting in the development of federated meta-learning models. These models aim to train personalized models for each client, reducing performance discrepancies across different clients and enhancing model fairness. In order to advance research on federated meta-learning and its applications in the field of cyberspace security, this paper first introduces the algorithms of federated meta-learning. Based on different usage principles, these algorithms are categorized into client-level personalization algorithms, network algorithms, prediction algorithms, and recommendation algorithms, and are thoroughly presented and analyzed. Subsequently, the paper divides current cyberspace security issues in the network domain into three branches: information content security, network security, and information system security. For each branch, the application research methods and achievements of federated meta-learning are elucidated and compared, highlighting the advantages and disadvantages of federated meta-learning in addressing different cyberspace security issues. Finally, the paper concludes with an outlook on the deep application of federated meta-learning in the field of cyberspace security.
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Ennaji, El Mahfoud, Salah El Hajla, Yassine Maleh, and Soufyane Mounir. "Adversarially robust federated deep learning models for intrusion detection in IoT." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 2 (February 1, 2025): 937. http://dx.doi.org/10.11591/ijeecs.v37.i2.pp937-947.
Повний текст джерелаАнотація:
<span>Ensuring the robustness, security, and privacy of machine learning is a pivotal objective, crucial for unlocking the complete potential of the internet of things (IoT). Deep neural networks have proven to be vulnerable to adversarial perturbations imperceptible to humans. These perturbations can give rise to adversarial attacks, leading to erroneous predictions by deep neural networks, particularly in intrusion detection within the IoT environment. This paper introduces a federated adversarial learning framework designed to protect both data privacy and deep neural network models. This framework consists of federated learning for data privacy and adversarial training on IoT devices to enhance model robustness. The experiments show that adversarial training at the Fog node devices significantly improves the robustness of a federated learning model against adversarial attacks when compared to normal training. Furthermore, the proposed adversarial deep federated learning model is validated using the Edge-IIoTset dataset, achieving an accuracy rate of 91.23% in the detection of attacks.</span>
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Chen, Naiyue, Yi Jin, Yinglong Li, and Luxin Cai. "Trust-based federated learning for network anomaly detection." Web Intelligence 19, no. 4 (January 20, 2022): 317–27. http://dx.doi.org/10.3233/web-210475.
Повний текст джерелаАнотація:
With the rapid development of social networks and the massive popularity of intelligent mobile terminals, network anomaly detection is becoming increasingly important. In daily work and life, edge nodes store a large number of network local connection data and audit data, which can be used to analyze network abnormal behavior. With the increasingly close network communication, the amount of network connection and other related data collected by each network terminal is increasing. Machine learning has become a classification method to analyze the features of big data in the network. Face to the problems of excessive data and long response time for network anomaly detection, we propose a trust-based Federated learning anomaly detection algorithm. We use the edge nodes to train the local data model, and upload the machine learning parameters to the central node. Meanwhile, according to the performance of edge nodes training, we set different weights to match the processing capacity of each terminal which will obtain faster convergence speed and better attack classification accuracy. The user’s private information will only be processed locally and will not be uploaded to the central server, which can reduce the risk of information disclosure. Finally, we compare the basic federated learning model and TFCNN algorithm on KDD Cup 99 dataset and MNIST dataset. The experimental results show that the TFCNN algorithm can improve accuracy and communication efficiency.
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Mani, Sathishkumar, Parasuram Chandrasekaran Kishoreraja, Christeena Joseph, Reji Manoharan, and Prasannavenkatesan Theerthagiri. "Hybrid intrusion detection model for hierarchical wireless sensor network using federated learning." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (February 1, 2025): 492. http://dx.doi.org/10.11591/ijai.v14.i1.pp492-499.
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<p class="Abstract">The applications of wireless sensor networks are vast and popular in today’s technology world. These networks consist of small, independent sensors that are capable of measuring various physical quantities. Deployment of wireless sensor networks increased due to immense applications which are susceptible to different types of attacks in an unprotected and open region. Intrusion detection systems (IDS) play a vital part in any secured environment for any network. IDS using federated learning have the potential to achieve better classification accuracy. Usually, all the data is stored in centralized server in order to communicate between the systems. On the other hand, federated learning is a distributed learning technique that does not transfer data but trains models locally and transfers the parameters to the centralized server. The proposed research uses a hybrid IDS for wireless sensor networks using federating learning. The detection takes place in real-time through detailed analysis of attacks at different levels in a decentralized manner. Hybrid IDS are designed for node level, cluster level and the base station where federated learning acts as a client and aggregated server.</p>
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Dongkyun Kim, Gicheol Wang, GiSung Yoo, SeungHae Kim, and OkHwan Byeon. "Media-Specific Network Service Environment on Federated Autonomous Distributed Networks." International Journal of Advancements in Computing Technology 5, no. 1 (January 15, 2013): 659–67. http://dx.doi.org/10.4156/ijact.vol5.issue1.73.
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Dahir, Mohamed Haji, Hadi Alizadeh, and Didem Gözüpek. "Energy efficient virtual network embedding for federated software-defined networks." International Journal of Communication Systems 32, no. 6 (February 19, 2019): e3912. http://dx.doi.org/10.1002/dac.3912.
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Wang, Yunhui, Weichu Zheng, Zifei Liu, Jinyan Wang, Hongjian Shi, Mingyu Gu, and Yicheng Di. "A Federated Network Intrusion Detection System with Multi-Branch Network and Vertical Blocking Aggregation." Electronics 12, no. 19 (September 27, 2023): 4049. http://dx.doi.org/10.3390/electronics12194049.
Повний текст джерелаАнотація:
The rapid development of cloud–fog–edge computing and mobile devices has led to massive amounts of data being generated. Also, artificial intelligence technology, like machine learning and deep learning, is widely used to mine the value of the data. Specifically, detecting attacks on the cloud–fog–edge computing system using mobile devices is essential. External attacks on network press organizations led to anomaly flow in network traffic. The network intrusion detection system (NIDS) has been an effective method for detecting anomaly flow. However, the NIDS is hard to deploy in distributed networks because network flow data are kept private. Existing methods cannot obtain an accurate NIDS under such a federated scenario. To construct an NIDS while preserving data privacy, we propose a combined model that integrates binary classifiers into a whole network based on simple classifier networks to specify the type of attack on anomalous data and offer instruction to other security system components. We also introduce federated learning (FL) methods into our system and design a new aggregation algorithm named vertical blocking aggregation (FedVB) according to our model structure. Our experiments demonstrate that our system can be more effective than simple multi-classifiers in terms of accuracy and significantly reduce communication and computation overhead when applying FedVB.
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Fan, Kefeng, Cun Xu, Xuguang Cao, Kaijie Jiao, and Wei Mo. "Tri-branch feature pyramid network based on federated particle swarm optimization for polyp segmentation." Mathematical Biosciences and Engineering 21, no. 1 (2024): 1610–24. http://dx.doi.org/10.3934/mbe.2024070.
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<abstract><p>Deep learning technology has shown considerable potential in various domains. However, due to privacy issues associated with medical data, legal and ethical constraints often result in smaller datasets. The limitations of smaller datasets hinder the applicability of deep learning technology in the field of medical image processing. To address this challenge, we proposed the Federated Particle Swarm Optimization algorithm, which is designed to increase the efficiency of decentralized data utilization in federated learning and to protect privacy in model training. To stabilize the federated learning process, we introduced Tri-branch feature pyramid network (TFPNet), a multi-branch structure model. TFPNet mitigates instability during the aggregation model deployment and ensures fast convergence through its multi-branch structure. We conducted experiments on four different public datasets$ \colon $ CVC-ClinicDB, Kvasir, CVC-ColonDB and ETIS-LaribPolypDB. The experimental results show that the Federated Particle Swarm Optimization algorithm outperforms single dataset training and the Federated Averaging algorithm when using independent scattered data, and TFPNet converges faster and achieves superior segmentation accuracy compared to other models.</p></abstract>
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R. Sushmitha. "Adaptive Blockchain-Integrated Nonlinear Federated Learning Framework for Real-Time Intrusion Detection in IoT Fog Networks ABFL-RTID." Communications on Applied Nonlinear Analysis 32, no. 1s (October 26, 2024): 105–21. http://dx.doi.org/10.52783/cana.v32.2113.
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This research presents the Adaptive Blockchain-Integrated Nonlinear Federated Learning (ABFL-RTID) model, designed for real-time intrusion detection in IoT fog networks. The framework leverages nonlinear learning techniques, such as deep neural networks, to enhance detection capabilities in complex and dynamic network environments. Integrating blockchain ensures decentralized security, while federated learning preserves data privacy by enabling local model training on edge devices. The nonlinear models improve adaptability, accurately identifying sophisticated intrusion patterns while securely validating updates through blockchain. Simulations show the framework achieving 96.8% detection accuracy with response times of 150 ms, demonstrating superior scalability and adaptability under changing network conditions. This study provides a practical approach for building resilient and secure intrusion detection systems, enhancing data integrity and privacy without the delays of traditional, centralized models.
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Wang, Xiujuan, Kangmiao Chen, Keke Wang, Zhengxiang Wang, Kangfeng Zheng, and Jiayue Zhang. "FedKG: A Knowledge Distillation-Based Federated Graph Method for Social Bot Detection." Sensors 24, no. 11 (May 28, 2024): 3481. http://dx.doi.org/10.3390/s24113481.
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Malicious social bots pose a serious threat to social network security by spreading false information and guiding bad opinions in social networks. The singularity and scarcity of single organization data and the high cost of labeling social bots have given rise to the construction of federated models that combine federated learning with social bot detection. In this paper, we first combine the federated learning framework with the Relational Graph Convolutional Neural Network (RGCN) model to achieve federated social bot detection. A class-level cross entropy loss function is applied in the local model training to mitigate the effects of the class imbalance problem in local data. To address the data heterogeneity issue from multiple participants, we optimize the classical federated learning algorithm by applying knowledge distillation methods. Specifically, we adjust the client-side and server-side models separately: training a global generator to generate pseudo-samples based on the local data distribution knowledge to correct the optimization direction of client-side classification models, and integrating client-side classification models’ knowledge on the server side to guide the training of the global classification model. We conduct extensive experiments on widely used datasets, and the results demonstrate the effectiveness of our approach in social bot detection in heterogeneous data scenarios. Compared to baseline methods, our approach achieves a nearly 3–10% improvement in detection accuracy when the data heterogeneity is larger. Additionally, our method achieves the specified accuracy with minimal communication rounds.
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Zhao, Zhuoyue, Feiyu Wu, Chao Dong, and Yuben Qu. "Embedded Implementation and Evaluation of Deep Neural Network of Federated Learning." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 687–94. http://dx.doi.org/10.54097/hset.v39i.6628.
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Compared with traditional distributed machine learning, federated learning (or joint learning) enables multiple computing nodes to cooperate and train a shared machine learning model without transmitting original data. At present, the research work of federated learning mainly focuses on the theoretical method, and the system implementation is less, and only for the text data or simple image such as medical institution information sharing, handwriting font recognition and other simple neural network applications. Aiming at more complex deep neural networks, this project implements a multi-node federated learning system on embedded device, and evaluates its key performance indicators such as training accuracy, delay and loss. The research method mainly uses embedded computer both as client and server, adjusts and groups the Visdrone datasets as training samples, and then trains the model on the client based on YOLOv4 algorithm, realizes the encrypted transmission of information through TCP protocol, and achieves the aggregation update of the model on the server with FedAvg algorithm.
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Wang, Weidong, Siqi Li, Jihao Zhang, Dan Shan, Guangwei Zhang, and Xiang Gao. "A Node Selection Strategy in Space-Air-Ground Information Networks: A Double Deep Q-Network Based on the Federated Learning Training Method." Remote Sensing 16, no. 4 (February 9, 2024): 651. http://dx.doi.org/10.3390/rs16040651.
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The Space-Air-Ground Information Network (SAGIN) provides extensive coverage, enabling global connectivity across a diverse array of sensors, devices, and objects. These devices generate large amounts of data that require advanced analytics and decision making using artificial intelligence techniques. However, traditional deep learning approaches encounter drawbacks, primarily, the requirement to transmit substantial volumes of raw data to central servers, which raises concerns about user privacy breaches during transmission. Federated learning (FL) has emerged as a viable solution to these challenges, addressing both data volume and privacy issues effectively. Nonetheless, the deployment of FL faces its own set of obstacles, notably the excessive delay and energy consumption caused by the vast number of devices and fluctuating channel conditions. In this paper, by considering the heterogeneity of devices and the instability of the network state, the delay and energy consumption models of each round of federated training are established. Subsequently, we introduce a strategic node selection approach aimed at minimizing training costs. Building upon this, we propose an innovative, empirically driven Double Deep Q Network (DDQN)-based algorithm called low-cost node selection in federated learning (LCNSFL). The LCNSFL algorithm can assist edge servers in selecting the optimal set of devices to participate in federated training before the start of each round, based on the collected system state information. This paper culminates with a simulation-based comparison, showcasing the superior performance of LCNSFL against existing algorithms, thus underscoring its efficacy in practical applications.
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Xiaoyu Lan, Jalil Taghia, Farnaz Moradi, Mohammad Ali Khoshkholghi, Edvin Listo Zec, Olof Mogren, Toktam Mahmoodi, and Andreas Johnsson. "Federated learning for performance prediction in multi-operator environments." ITU Journal on Future and Evolving Technologies 4, no. 1 (March 10, 2023): 166–77. http://dx.doi.org/10.52953/pfyz9165.
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Telecom vendors and operators deliver services with strict requirements on performance, over complex and sometimes partly shared network infrastructures. A key enabler for network and service management in such environments is knowledge sharing, and the use of data-driven models for performance prediction, forecasting, and troubleshooting. In this paper, we outline a multi-operator service metrics prediction framework using federated learning that allows privacy-preserved knowledge-sharing across operators for improved model performance, and also reduced requirements on data transfer within an operator network. Federated learning is compared against local and central learning strategies for multi-operator performance prediction, and it is shown to balance the requirements on data privacy, model performance, and the network overhead. Further, the paper provides insights on how data heterogeneity affects model performance, where the conclusion is that standard federated learning has certain robustness to data heterogeneity. Finally, we discuss the challenges related to training a federated learning model with a limited budget on the communication rounds. The evaluation is performed using a set of realistic publicly available data traces, that are adapted specifically for the purpose of studying multi-operator service performance prediction.
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Jiang, Jingyan, Liang Hu, Chenghao Hu, Jiate Liu, and Zhi Wang. "BACombo—Bandwidth-Aware Decentralized Federated Learning." Electronics 9, no. 3 (March 5, 2020): 440. http://dx.doi.org/10.3390/electronics9030440.
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The emerging concern about data privacy and security has motivated the proposal of federated learning. Federated learning allows computing nodes to only synchronize the locally- trained models instead of their original data in distributed training. Conventional federated learning architecture, inherited from the parameter server design, relies on highly centralized typologies and large nodes-to-server bandwidths. However, in real-world federated learning scenarios, the network capacities between nodes are highly uniformly distributed and smaller than that in data centers. As a result, how to efficiently utilize network capacities between computing nodes is crucial for conventional federated learning. In this paper, we propose Bandwidth Aware Combo (BACombo), a model segment level decentralized federated learning, to tackle this problem. In BACombo, we propose a segmented gossip aggregation mechanism that makes full use of node-to-node bandwidth for speeding up the communication time. Besides, a bandwidth-aware worker selection model further reduces the transmission delay by greedily choosing the bandwidth-sufficient worker. The convergence guarantees are provided for BACombo. The experimental results on various datasets demonstrate that the training time is reduced by up to 18 times that of baselines without accuracy degrade.
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Duan, Shaoming, Chuanyi Liu, Peiyi Han, Xiaopeng Jin, Xinyi Zhang, Xiayu Xiang, and Hezhong Pan. "Fed-DNN-Debugger: Automatically Debugging Deep Neural Network Models in Federated Learning." Security and Communication Networks 2023 (February 23, 2023): 1–14. http://dx.doi.org/10.1155/2023/5968168.
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Federated learning is a distributed machine learning framework that has been widely applied in scenarios that require data privacy. To obtain a neural network model that performs well, when the model falls into a bug, existing solutions retrain it on a larger training dataset or the carefully selected samples from model diagnosis. To overcome this challenge, this paper presents Fed-DNN-Debugger, which can automatically and efficiently fix DNN models in federated learning. Fed-DNN-Debugger fixes the federated model by fixing each client model. Fed-DNN-Debugger consists of two modules for debugging a client model: nonintrusive metadata capture (NIMC) and automated neural network model debugging (ANNMD). NIMC collects the metadata with deep learning software syntax automatically. It does not insert any code for metadata collection into modeling scripts. ANNMD scores samples according to metadata and searches for high-quality samples. Models are retrained on the selected samples to repair their weights. Our experiments with popular federated models show that Fed-DNN-Debugger can improve the test accuracy by 8% by automatically fixing models.
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Gao, Fuwei, Chuanting Zhang, Jingping Qiao, Kaiqiang Li, and Yi Cao. "Communication-Efficient Wireless Traffic Prediction with Federated Learning." Mathematics 12, no. 16 (August 17, 2024): 2539. http://dx.doi.org/10.3390/math12162539.
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Wireless traffic prediction is essential to developing intelligent communication networks that facilitate efficient resource allocation. Along this line, decentralized wireless traffic prediction under the paradigm of federated learning is becoming increasingly significant. Compared to traditional centralized learning, federated learning satisfies network operators’ requirements for sensitive data protection and reduces the consumption of network resources. In this paper, we propose a novel communication-efficient federated learning framework, named FedCE, by developing a gradient compression scheme and an adaptive aggregation strategy for wireless traffic prediction. FedCE achieves gradient compression through top-K sparsification and can largely relieve the communication burdens between local clients and the central server, making it communication-efficient. An adaptive aggregation strategy is designed by quantifying the different contributions of local models to the global model, making FedCE aware of spatial dependencies among various local clients. We validate the effectiveness of FedCE on two real-world datasets. The results demonstrate that FedCE can improve prediction accuracy by approximately 27% with only 20% of communications in the baseline method.
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Kim, Eun-ji, and Eun-Kyu Lee. "Evaluating the Impact of Mobility on Differentially Private Federated Learning." Applied Sciences 14, no. 12 (June 17, 2024): 5245. http://dx.doi.org/10.3390/app14125245.
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This paper investigates differential privacy in federated learning. This topic has been actively examined in conventional network environments, but few studies have investigated it in the Internet of Vehicles, especially considering various mobility patterns. In particular, this work aims to measure and enumerate the trade-off between accuracy of performance and the level of data protection and evaluate how mobility patterns affect it. To this end, this paper proposes a method considering three factors: learning models, vehicle mobility, and a privacy algorithm. By taking into account mobility patterns, local differential privacy is enhanced with an adaptive clipping method and applied to a mobility-based federated learning model. Experiments run the model on vehicular networks with two different mobility scenarios representing a non-accident traffic situation and traffic events, respectively. Results show that our privacy-enhanced federated learning models degrade accuracy performance by 2.96–3.26% on average, which is compared to the performance drop (42.97% on average) in conventional federated learning models.
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Wang, Derui, Sheng Wen, Alireza Jolfaei, Mohammad Sayad Haghighi, Surya Nepal, and Yang Xiang. "On the Neural Backdoor of Federated Generative Models in Edge Computing." ACM Transactions on Internet Technology 22, no. 2 (May 31, 2022): 1–21. http://dx.doi.org/10.1145/3425662.
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Edge computing, as a relatively recent evolution of cloud computing architecture, is the newest way for enterprises to distribute computational power and lower repetitive referrals to central authorities. In the edge computing environment, Generative Models (GMs) have been found to be valuable and useful in machine learning tasks such as data augmentation and data pre-processing. Federated learning and distributed learning refer to training machine learning models in the edge computing network. However, federated learning and distributed learning also bring additional risks to GMs since all peers in the network have access to the model under training. In this article, we study the vulnerabilities of federated GMs to data-poisoning-based backdoor attacks via gradient uploading. We additionally enhance the attack to reduce the required poisonous data samples and cope with dynamic network environments. Last but not least, the attacks are formally proven to be stealthy and effective toward federated GMs. According to the experiments, neural backdoors can be successfully embedded by including merely 5\% poisonous samples in the local training dataset of an attacker.
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Juan, Pin-Hung, and Ja-Ling Wu. "Enhancing Communication Efficiency and Training Time Uniformity in Federated Learning through Multi-Branch Networks and the Oort Algorithm." Algorithms 17, no. 2 (January 23, 2024): 52. http://dx.doi.org/10.3390/a17020052.
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In this study, we present a federated learning approach that combines a multi-branch network and the Oort client selection algorithm to improve the performance of federated learning systems. This method successfully addresses the significant issue of non-iid data, a challenge not adequately tackled by the commonly used MFedAvg method. Additionally, one of the key innovations of this research is the introduction of uniformity, a metric that quantifies the disparity in training time amongst participants in a federated learning setup. This novel concept not only aids in identifying stragglers but also provides valuable insights into assessing the fairness and efficiency of the system. The experimental results underscore the merits of the integrated multi-branch network with the Oort client selection algorithm and highlight the crucial role of uniformity in designing and evaluating federated learning systems.
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Paragliola, Giovanni, Patrizia Ribino, and Zaib Ullah. "A Federated Learning Approach to Support the Decision-Making Process for ICU Patients in a European Telemedicine Network." Journal of Sensor and Actuator Networks 12, no. 6 (November 20, 2023): 78. http://dx.doi.org/10.3390/jsan12060078.
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A result of the pandemic is an urgent need for data collaborations that empower the clinical and scientific communities in responding to rapidly evolving global challenges. The ICU4Covid project joined research institutions, medical centers, and hospitals all around Europe in a telemedicine network for sharing capabilities, knowledge, and expertise distributed within the network. However, healthcare data sharing has ethical, regulatory, and legal complexities that pose several restrictions on their access and use. To mitigate this issue, the ICU4Covid project integrates a federated learning architecture, allowing distributed machine learning within a cross-institutional healthcare system without the data being transported or exposed outside their original location. This paper presents the federated learning approach to support the decision-making process for ICU patients in a European telemedicine network. The proposed approach was applied to the early identification of high-risk hypertensive patients. Experimental results show how the knowledge of every single node is spread within the federation, improving the ability of each node to make an early prediction of high-risk hypertensive patients. Moreover, a performance evaluation shows an accuracy and precision of over 90%, confirming a good performance of the FL approach as a prediction test. The FL approach can significantly support the decision-making process for ICU patients in distributed networks of federated healthcare organizations.
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Feng, Jian, Cailing Du, and Qi Mu. "Traffic Flow Prediction Based on Federated Learning and Spatio-Temporal Graph Neural Networks." ISPRS International Journal of Geo-Information 13, no. 6 (June 18, 2024): 210. http://dx.doi.org/10.3390/ijgi13060210.
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In response to the insufficient consideration of spatio-temporal dependencies and traffic pattern similarity in traffic flow prediction methods based on federated learning, as well as the neglect of model heterogeneity and objective heterogeneity, a traffic flow prediction model based on federated learning and spatio-temporal graph neural networks is proposed. The model is divided into two stages. In the road network division stage, the traffic road network is divided into subnetworks by the dynamic time warping algorithm and the K-means algorithm, to ensure the same subnetwork has the similar traffic flow pattern. The federated learning stage is divided into two sub-stages. In the local training phase, the spatio-temporal graph neural network with an attention mechanism is utilized to create personalized models and meme models to capture the spatio-temporal dependencies of each subnetwork. At the same time, deep mutual learning is utilized to address model heterogeneity and objective heterogeneity through knowledge distillation. In the global aggregation phase, a multi-factor weighted aggregation strategy is designed to measure the contribution of each local model to the global model, to enhance the fairness of aggregation. Three sets of experiments were conducted on two real datasets, and the experimental results demonstrate that the proposed model outperforms the baseline models in three common evaluation metrics.
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Macedo, Daniel, Danilo Santos, Angelo Perkusich, and Dalton C. G. Valadares. "Mobility-Aware Federated Learning Considering Multiple Networks." Sensors 23, no. 14 (July 10, 2023): 6286. http://dx.doi.org/10.3390/s23146286.
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Federated learning (FL) is a distributed training method for machine learning models (ML) that maintain data ownership on users. However, this distributed training approach can lead to variations in efficiency due to user behaviors or characteristics. For instance, mobility can hinder training by causing a client dropout when a device loses connection with other devices on the network. To address this issue, we propose a FL coordination algorithm, MoFeL, to ensure efficient training even in scenarios with mobility. Furthermore, MoFeL evaluates multiple networks with different central servers. To evaluate its effectiveness, we conducted simulation experiments using an image classification application that utilizes machine models trained by a convolutional neural network. The simulation results demonstrate that MoFeL outperforms traditional training coordination algorithms in FL, with 156.5% more training cycles, in scenarios with high mobility compared to an algorithm that does not consider mobility aspects.
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Ma, Chuang, Xin Ren, Guangxia Xu, and Bo He. "FedGR: Federated Graph Neural Network for Recommendation Systems." Axioms 12, no. 2 (February 7, 2023): 170. http://dx.doi.org/10.3390/axioms12020170.
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Social recommendation systems based on the graph neural network (GNN) have received a lot of research-related attention recently because they can use social information to improve recommendation accuracy and because of the benefits derived from the excellent performance of the graph neural network in graphic data modeling. A large number of excellent studies in this area have been proposed one after another, but they all share a common requirement that the data should be centrally stored. In recent years, there have been growing concerns about data privacy. At the same time, the introduction of numerous stringent data protection regulations, represented by general data protection regulations (GDPR), has challenged the recommendation models with conventional centralized data storage. For the above reasons, we have designed a flexible model of recommendation algorithms for social scenarios based on federated learning. We call it the federated graph neural network for recommendation systems (FedGR). Previous related work in this area has only considered GNN, social networks, and federated learning separately. Our work is the first to consider all three together, and we have carried out a detailed design for each part. In FedGR, we used the graph attention network to assist in modeling the implicit vector representation learned by users from social relationship graphs and historical item graphs. In order to protect data privacy, we used FedGR flexible data privacy protection by incorporating traditional cryptography encryption techniques with the proposed “noise injection” strategy, which enables FedGR to ensure data privacy while minimizing the loss of recommended performance. We also demonstrate a different learning paradigm for the recommendation model under federation. Our proposed work has been validated on two publicly available popular datasets. According to the experimental results, FedGR has decreased MAE and RMSE compared with previous work, which proves its rationality and effectiveness.
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Toldinas, Jevgenijus, Algimantas Venčkauskas, Agnius Liutkevičius, and Nerijus Morkevičius. "Framing Network Flow for Anomaly Detection Using Image Recognition and Federated Learning." Electronics 11, no. 19 (September 30, 2022): 3138. http://dx.doi.org/10.3390/electronics11193138.
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The intrusion detection system (IDS) must be able to handle the increase in attack volume, increasing Internet traffic, and accelerating detection speeds. Network flow feature (NTF) records are the input of flow-based IDSs that are used to determine whether network traffic is normal or malicious in order to avoid IDS from difficult and time-consuming packet content inspection processing since only flow records are examined. To reduce computational power and training time, this paper proposes a novel pre-processing method merging a specific amount of NTF records into frames, and frame transformation into images. Federated learning (FL) enables multiple users to share the learned models while maintaining the privacy of their training data. This research suggests federated transfer learning and federated learning methods for NIDS employing deep learning for image classification and conducting tests on the BOUN DDoS dataset to address the issue of training data privacy. Our experimental results indicate that the proposed Federated transfer learning (FTL) and FL methods for training do not require data centralization and preserve participant data privacy while achieving acceptable accuracy in DDoS attack identification: FTL (92.99%) and FL (88.42%) in comparison with Traditional transfer learning (93.95%).
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Zheng, Longfei, Jun Zhou, Chaochao Chen, Bingzhe Wu, Li Wang, and Benyu Zhang. "ASFGNN: Automated separated-federated graph neural network." Peer-to-Peer Networking and Applications 14, no. 3 (February 5, 2021): 1692–704. http://dx.doi.org/10.1007/s12083-021-01074-w.
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Liu, Shengli, Guanding Yu, Rui Yin, and Jiantao Yuan. "Adaptive Network Pruning for Wireless Federated Learning." IEEE Wireless Communications Letters 10, no. 7 (July 2021): 1572–76. http://dx.doi.org/10.1109/lwc.2021.3074605.
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Le, Junqing, Xinyu Lei, Nankun Mu, Hengrun Zhang, Kai Zeng, and Xiaofeng Liao. "Federated Continuous Learning With Broad Network Architecture." IEEE Transactions on Cybernetics 51, no. 8 (August 2021): 3874–88. http://dx.doi.org/10.1109/tcyb.2021.3090260.
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Riley, George F., Mostafa H. Ammar, Richard M. Fujimoto, Alfred Park, Kalyan Perumalla, and Donghua Xu. "A federated approach to distributed network simulation." ACM Transactions on Modeling and Computer Simulation 14, no. 2 (April 2004): 116–48. http://dx.doi.org/10.1145/985793.985795.
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Castiglione, Aniello, Francesco Palmieri, and Kim-Kwang Raymond Choo. "Enhanced Network Support for Federated Cloud Infrastructures." IEEE Cloud Computing 3, no. 3 (May 2016): 16–23. http://dx.doi.org/10.1109/mcc.2016.59.
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Wang, Ganggui, Celimuge Wu, Zhaoyang Du, Tsutomu Yoshinaga, Rui Yin, and Lei Zhong. "DRL-Assisted Network Selection for Federated IoV." IEEE Internet of Things Magazine 6, no. 3 (September 2023): 86–90. http://dx.doi.org/10.1109/iotm.001.2300080.
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