Journal articles: 'Federated learning applications'

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Saha, Sudipan, and Tahir Ahmad. "Federated transfer learning: Concept and applications." Intelligenza Artificiale 15, no. 1 (July 28, 2021): 35–44. http://dx.doi.org/10.3233/ia-200075.

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Development of Artificial Intelligence (AI) is inherently tied to the development of data. However, in most industries data exists in form of isolated islands, with limited scope of sharing between different organizations. This is an hindrance to the further development of AI. Federated learning has emerged as a possible solution to this problem in the last few years without compromising user privacy. Among different variants of the federated learning, noteworthy is federated transfer learning (FTL) that allows knowledge to be transferred across domains that do not have many overlapping features and users. In this work we provide a comprehensive survey of the existing works on this topic. In more details, we study the background of FTL and its different existing applications. We further analyze FTL from privacy and machine learning perspective.

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Launet, Laëtitia, Yuandou Wang, Adrián Colomer, Jorge Igual, Cristian Pulgarín-Ospina, Spiros Koulouzis, Riccardo Bianchi, et al. "Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions." Applied Sciences 13, no. 2 (January 9, 2023): 919. http://dx.doi.org/10.3390/app13020919.

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Deep learning-based algorithms have led to tremendous progress over the last years, but they face a bottleneck as their optimal development highly relies on access to large datasets. To mitigate this limitation, cross-silo federated learning has emerged as a way to train collaborative models among multiple institutions without having to share the raw data used for model training. However, although artificial intelligence experts have the expertise to develop state-of-the-art models and actively share their code through notebook environments, implementing a federated learning system in real-world applications entails significant engineering and deployment efforts. To reduce the complexity of federation setups and bridge the gap between federated learning and notebook users, this paper introduces a solution that leverages the Jupyter environment as part of the federated learning pipeline and simplifies its automation, the Notebook Federator. The feasibility of this approach is then demonstrated with a collaborative model solving a digital pathology image analysis task in which the federated model reaches an accuracy of 0.8633 on the test set, as compared to the centralized configurations for each institution obtaining 0.7881, 0.6514, and 0.8096, respectively. As a fast and reproducible tool, the proposed solution enables the deployment of a cross-country federated environment in only a few minutes.

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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.

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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.

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Li, Li, Yuxi Fan, Mike Tse, and Kuo-Yi Lin. "A review of applications in federated learning." Computers & Industrial Engineering 149 (November 2020): 106854. http://dx.doi.org/10.1016/j.cie.2020.106854.

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Amiri, Mohammad Mohammadi, Tolga M. Duman, Deniz Gunduz, Sanjeev R. Kulkarni, and H. Vincent Poor Poor. "Blind Federated Edge Learning." IEEE Transactions on Wireless Communications 20, no. 8 (August 2021): 5129–43. http://dx.doi.org/10.1109/twc.2021.3065920.

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Fu, Xingbo, Binchi Zhang, Yushun Dong, Chen Chen, and Jundong Li. "Federated Graph Machine Learning." ACM SIGKDD Explorations Newsletter 24, no. 2 (November 29, 2022): 32–47. http://dx.doi.org/10.1145/3575637.3575644.

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Graph machine learning has gained great attention in both academia and industry recently. Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are trained over massive graph data. However, in many realworld scenarios, such as hospitalization prediction in healthcare systems, the graph data is usually stored at multiple data owners and cannot be directly accessed by any other parties due to privacy concerns and regulation restrictions. Federated Graph Machine Learning (FGML) is a promising solution to tackle this challenge by training graph machine learning models in a federated manner. In this survey, we conduct a comprehensive review of the literature in FGML. Specifically, we first provide a new taxonomy to divide the existing problems in FGML into two settings, namely, FL with structured data and structured FL. Then, we review the mainstream techniques in each setting and elaborate on how they address the challenges under FGML. In addition, we summarize the real-world applications of FGML from different domains and introduce open graph datasets and platforms adopted in FGML. Finally, we present several limitations in the existing studies with promising research directions in this field.

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Liu, Yang, Anbu Huang, Yun Luo, He Huang, Youzhi Liu, Yuanyuan Chen, Lican Feng, Tianjian Chen, Han Yu, and Qiang Yang. "FedVision: An Online Visual Object Detection Platform Powered by Federated Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 08 (April 3, 2020): 13172–79. http://dx.doi.org/10.1609/aaai.v34i08.7021.

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Visual object detection is a computer vision-based artificial intelligence (AI) technique which has many practical applications (e.g., fire hazard monitoring). However, due to privacy concerns and the high cost of transmitting video data, it is highly challenging to build object detection models on centrally stored large training datasets following the current approach. Federated learning (FL) is a promising approach to resolve this challenge. Nevertheless, there currently lacks an easy to use tool to enable computer vision application developers who are not experts in federated learning to conveniently leverage this technology and apply it in their systems. In this paper, we report FedVision - a machine learning engineering platform to support the development of federated learning powered computer vision applications. The platform has been deployed through a collaboration between WeBank and Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. Over four months of usage, it has achieved significant efficiency improvement and cost reduction while removing the need to transmit sensitive data for three major corporate customers. To the best of our knowledge, this is the first real application of FL in computer vision-based tasks.

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Mun, Hyunsu, and Youngseok Lee. "Internet Traffic Classification with Federated Learning." Electronics 10, no. 1 (December 28, 2020): 27. http://dx.doi.org/10.3390/electronics10010027.

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As Internet traffic classification is a typical problem for ISPs or mobile carriers, there have been a lot of studies based on statistical packet header information, deep packet inspection, or machine learning. Due to recent advances in end-to-end encryption and dynamic port policies, machine or deep learning has been an essential key to improve the accuracy of packet classification. In addition, ISPs or mobile carriers should carefully deal with the privacy issue while collecting user packets for accounting or security. The recent development of distributed machine learning, called federated learning, collaboratively carries out machine learning jobs on the clients without uploading data to a central server. Although federated learning provides an on-device learning framework towards user privacy protection, its feasibility and performance of Internet traffic classification have not been fully examined. In this paper, we propose a federated-learning traffic classification protocol (FLIC), which can achieve an accuracy comparable to centralized deep learning for Internet application identification without privacy leakage. FLIC can classify new applications on-the-fly when a participant joins in learning with a new application, which has not been done in previous works. By implementing the prototype of FLIC clients and a server with TensorFlow, the clients gather packets, perform the on-device training job and exchange the training results with the FLIC server. In addition, we demonstrate that federated learning-based packet classification achieves an accuracy of 88% under non-independent and identically distributed (non-IID) traffic across clients. When a new application that can be classified dynamically as a client participates in learning was added, an accuracy of 92% was achieved.

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Yang, Qiang. "Toward Responsible AI: An Overview of Federated Learning for User-centered Privacy-preserving Computing." ACM Transactions on Interactive Intelligent Systems 11, no. 3-4 (December 31, 2021): 1–22. http://dx.doi.org/10.1145/3485875.

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With the rapid advances of Artificial Intelligence (AI) technologies and applications, an increasing concern is on the development and application of responsible AI technologies. Building AI technologies or machine-learning models often requires massive amounts of data, which may include sensitive, user private information to be collected from different sites or countries. Privacy, security, and data governance constraints rule out a brute force process in the acquisition and integration of these data. It is thus a serious challenge to protect user privacy while achieving high-performance models. This article reviews recent progress of federated learning in addressing this challenge in the context of privacy-preserving computing. Federated learning allows global AI models to be trained and used among multiple decentralized data sources with high security and privacy guarantees, as well as sound incentive mechanisms. This article presents the background, motivations, definitions, architectures, and applications of federated learning as a new paradigm for building privacy-preserving, responsible AI ecosystems.

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Yang, Zhaohui, Mingzhe Chen, Kai-Kit Wong, H. Vincent Poor, and Shuguang Cui. "Federated Learning for 6G: Applications, Challenges, and Opportunities." Engineering 8 (January 2022): 33–41. http://dx.doi.org/10.1016/j.eng.2021.12.002.

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Hu, Sixu, Yuan Li, Xu Liu, Qinbin Li, Zhaomin Wu, and Bingsheng He. "The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems." ACM Transactions on Intelligent Systems and Technology 13, no. 4 (August 31, 2022): 1–32. http://dx.doi.org/10.1145/3510540.

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This article presents and characterizes an Open Application Repository for Federated Learning (OARF), a benchmark suite for federated machine learning systems. Previously available benchmarks for federated learning (FL) have focused mainly on synthetic datasets and use a limited number of applications. OARF mimics more realistic application scenarios with publicly available datasets as different data silos in image, text, and structured data. Our characterization shows that the benchmark suite is diverse in data size, distribution, feature distribution, and learning task complexity. The extensive evaluations with reference implementations show the future research opportunities for important aspects of FL systems. We have developed reference implementations, and evaluated the important aspects of FL, including model accuracy, communication cost, throughput, and convergence time. Through these evaluations, we discovered some interesting findings such as FL can effectively increase end-to-end throughput. The code of OARF is publicly available on GitHub. 1

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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.

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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.

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Mansouri, Mohamad, Melek Önen, Wafa Ben Jaballah, and Mauro Conti. "SoK: Secure Aggregation Based on Cryptographic Schemes for Federated Learning." Proceedings on Privacy Enhancing Technologies 2023, no. 1 (January 2023): 140–57. http://dx.doi.org/10.56553/popets-2023-0009.

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Secure aggregation consists of computing the sum of data collected from multiple sources without disclosing these individual inputs. Secure aggregation has been found useful for various applications ranging from electronic voting to smart grid measurements. Recently, federated learning emerged as a new collaborative machine learning technology to train machine learning models. In this work, we study the suitability of secure aggregation based on cryptographic schemes to federated learning. We first provide a formal definition of the problem and suggest a systematic categorization of existing solutions. We further investigate the specific challenges raised by federated learning and analyze the recent dedicated secure aggregation solutions based on cryptographic schemes. We finally share some takeaway messages that would help a secure design of federated learning and identify open research directions in this topic. Based on the takeaway messages, we propose an improved definition of secure aggregation that better fits federated learning.

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Yan, Xin, Yiming Qin, Xiaodong Hu, and Xiaoling Xiao. "Distributed consensus problem with caching on federated learning framework." International Journal of Distributed Sensor Networks 18, no. 4 (April 2022): 155013292210929. http://dx.doi.org/10.1177/15501329221092932.

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Federated learning framework facilitates more applications of deep learning algorithms on the existing network architectures, where the model parameters are aggregated in a centralized manner. However, some of federated learning participants are often inaccessible, such as in a power shortage or dormant state. That will force us to explore the possibility that the parameter aggregation is operated in an ad hoc manner, which is based on consensus computing. On the contrary, since caching mechanism is indispensable to any federated learning mobile node, it is necessary to investigate the connection between it and consensus computing. In this article, we first propose a novel federated learning paradigm, which supports an ad hoc operation mode for federated learning participants. Second, a discrete-time dynamic equation and its control law are formulated to satisfy the demands from federated learning framework, with a quantized caching scheme designed to mask the uncertainties from both asynchronous updates and measurement noises. Then, the consensus conditions and the convergence of the consensus protocol are deduced analytically, and a quantized caching strategy to optimize the convergence speed is provided. Our major contribution is to give the basic theories of distributed consensus problem for federated learning framework, and the theoretical results are validated by numerical simulations.

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Liu, Yang, Anbu Huang, Yun Luo, He Huang, Youzhi Liu, Yuanyuan Chen, Lican Feng, Tianjian Chen, Han Yu, and Qiang Yang. "Federated Learning-Powered Visual Object Detection for Safety Monitoring." AI Magazine 42, no. 2 (October 20, 2021): 19–27. http://dx.doi.org/10.1609/aimag.v42i2.15095.

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Visual object detection is an important artificial intelligence (AI) technique for safety monitoring applications. Current approaches for building visual object detection models require large and well-labeled dataset stored by a centralized entity. This not only poses privacy concerns under the General Data Protection Regulation (GDPR), but also incurs large transmission and storage overhead. Federated learning (FL) is a promising machine learning paradigm to address these challenges. In this paper, we report on FedVision—a machine learning engineering platform to support the development of federated learning powered computer vision applications—to bridge this important gap. The platform has been deployed through collaboration between WeBank and Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. Through actual usage, it has demonstrated significant efficiency improvement and cost reduction while fulfilling privacy-preservation requirements (e.g., reducing communication overhead for one company by 50 fold and saving close to 40,000RMB of network cost per annum). To the best of our knowledge, this is the first practical application of FL in computer vision-based tasks.

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Kim, Seong-Woong, and Dong-Wan Choi. "Stable Federated Learning with Dataset Condensation." Journal of Computing Science and Engineering 16, no. 1 (March 31, 2022): 52–62. http://dx.doi.org/10.5626/jcse.2022.16.1.52.

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Kim, Hyesung, Jihong Park, Mehdi Bennis, and Seong-Lyun Kim. "Blockchained On-Device Federated Learning." IEEE Communications Letters 24, no. 6 (June 2020): 1279–83. http://dx.doi.org/10.1109/lcomm.2019.2921755.

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Zhu, Hangyu, and Yaochu Jin. "Multi-Objective Evolutionary Federated Learning." IEEE Transactions on Neural Networks and Learning Systems 31, no. 4 (April 2020): 1310–22. http://dx.doi.org/10.1109/tnnls.2019.2919699.

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Zellinger, Werner, Volkmar Wieser, Mohit Kumar, David Brunner, Natalia Shepeleva, Rafa Gálvez, Josef Langer, Lukas Fischer, and Bernhard Moser. "Beyond federated learning: On confidentiality-critical machine learning applications in industry." Procedia Computer Science 180 (2021): 734–43. http://dx.doi.org/10.1016/j.procs.2021.01.296.

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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.

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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.

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Prayitno, Chi-Ren Shyu, Karisma Trinanda Putra, Hsing-Chung Chen, Yuan-Yu Tsai, K. S. M. Tozammel Hossain, Wei Jiang, and Zon-Yin Shae. "A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications." Applied Sciences 11, no. 23 (November 25, 2021): 11191. http://dx.doi.org/10.3390/app112311191.

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Recent advances in deep learning have shown many successful stories in smart healthcare applications with data-driven insight into improving clinical institutions’ quality of care. Excellent deep learning models are heavily data-driven. The more data trained, the more robust and more generalizable the performance of the deep learning model. However, pooling the medical data into centralized storage to train a robust deep learning model faces privacy, ownership, and strict regulation challenges. Federated learning resolves the previous challenges with a shared global deep learning model using a central aggregator server. At the same time, patient data remain with the local party, maintaining data anonymity and security. In this study, first, we provide a comprehensive, up-to-date review of research employing federated learning in healthcare applications. Second, we evaluate a set of recent challenges from a data-centric perspective in federated learning, such as data partitioning characteristics, data distributions, data protection mechanisms, and benchmark datasets. Finally, we point out several potential challenges and future research directions in healthcare applications.

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Kang, Jiawen, Zehui Xiong, Dusit Niyato, Yuze Zou, Yang Zhang, and Mohsen Guizani. "Reliable Federated Learning for Mobile Networks." IEEE Wireless Communications 27, no. 2 (April 2020): 72–80. http://dx.doi.org/10.1109/mwc.001.1900119.

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Pham, Quoc-Viet, Ming Zeng, Thien Huynh-The, Zhu Han, and Won-Joo Hwang. "Aerial Access Networks for Federated Learning: Applications and Challenges." IEEE Network 36, no. 3 (May 2022): 159–66. http://dx.doi.org/10.1109/mnet.013.2100311.

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Banabilah, Syreen, Moayad Aloqaily, Eitaa Alsayed, Nida Malik, and Yaser Jararweh. "Federated learning review: Fundamentals, enabling technologies, and future applications." Information Processing & Management 59, no. 6 (November 2022): 103061. http://dx.doi.org/10.1016/j.ipm.2022.103061.

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Shaheen, Momina, Muhammad Shoaib Farooq, Tariq Umer, and Byung-Seo Kim. "Applications of Federated Learning; Taxonomy, Challenges, and Research Trends." Electronics 11, no. 4 (February 21, 2022): 670. http://dx.doi.org/10.3390/electronics11040670.

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The federated learning technique (FL) supports the collaborative training of machine learning and deep learning models for edge network optimization. Although a complex edge network with heterogeneous devices having different constraints can affect its performance, this leads to a problem in this area. Therefore, some research can be seen to design new frameworks and approaches to improve federated learning processes. The purpose of this study is to provide an overview of the FL technique and its applicability in different domains. The key focus of the paper is to produce a systematic literature review of recent research studies that clearly describes the adoption of FL in edge networks. The search procedure was performed from April 2020 to May 2021 with a total initial number of papers being 7546 published in the duration of 2016 to 2020. The systematic literature synthesizes and compares the algorithms, models, and frameworks of federated learning. Additionally, we have presented the scope of FL applications in different industries and domains. It has been revealed after careful investigation of studies that 25% of the studies used FL in IoT and edge-based applications and 30% of studies implement the FL concept in the health industry, 10% for NLP, 10% for autonomous vehicles, 10% for mobile services, 10% for recommender systems, and 5% for FinTech. A taxonomy is also proposed on implementing FL for edge networks in different domains. Moreover, another novelty of this paper is that datasets used for the implementation of FL are discussed in detail to provide the researchers an overview of the distributed datasets, which can be used for employing FL techniques. Lastly, this study discusses the current challenges of implementing the FL technique. We have found that the areas of medical AI, IoT, edge systems, and the autonomous industry can adapt the FL in many of its sub-domains; however, the challenges these domains can encounter are statistical heterogeneity, system heterogeneity, data imbalance, resource allocation, and privacy.

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Stallmann, Morris, and Anna Wilbik. "On a Framework for Federated Cluster Analysis." Applied Sciences 12, no. 20 (October 17, 2022): 10455. http://dx.doi.org/10.3390/app122010455.

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Federated learning is becoming increasingly popular to enable automated learning in distributed networks of autonomous partners without sharing raw data. Many works focus on supervised learning, while the area of federated unsupervised learning, similar to federated clustering, is still less explored. In this paper, we introduce a federated clustering framework that solves three challenges: determine the number of global clusters in a federated dataset, obtain a partition of the data via a federated fuzzy c-means algorithm, and validate the clustering through a federated fuzzy Davies–Bouldin index. The complete framework is evaluated through numerical experiments on artificial and real-world datasets. The observed results are promising, as in most cases the federated clustering framework’s results are consistent with its nonfederated equivalent. Moreover, we embed an alternative federated fuzzy c-means formulation into our framework and observe that our formulation is more reliable in case the data are noni.i.d., while the performance is on par in the i.i.d. case.

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Jang, Suyeon, Hyun Woo Oh, Young Hyun Yoon, Dong Hyun Hwang, Won Sik Jeong, and Seung Eun Lee. "A Multi-Core Controller for an Embedded AI System Supporting Parallel Recognition." Micromachines 12, no. 8 (July 21, 2021): 852. http://dx.doi.org/10.3390/mi12080852.

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Recent advances in artificial intelligence (AI) technology encourage the adoption of AI systems for various applications. In most deployments, AI-based computing systems adopt the architecture in which the central server processes most of the data. This characteristic makes the system use a high amount of network bandwidth and can cause security issues. In order to overcome these issues, a new AI model called federated learning was presented. Federated learning adopts an architecture in which the clients take care of data training and transmit only the trained result to the central server. As the data training from the client abstracts and reduces the original data, the system operates with reduced network resources and reinforced data security. A system with federated learning supports a variety of client systems. To build an AI system with resource-limited client systems, composing the client system with multiple embedded AI processors is valid. For realizing the system with this architecture, introducing a controller to arbitrate and utilize the AI processors becomes a stringent requirement. In this paper, we propose an embedded AI system for federated learning that can be composed flexibly with the AI core depending on the application. In order to realize the proposed system, we designed a controller for multiple AI cores and implemented it on a field-programmable gate array (FPGA). The operation of the designed controller was verified through image and speech applications, and the performance was verified through a simulator.

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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.

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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.

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Amiri, Mohammad Mohammadi, and Deniz Gunduz. "Federated Learning Over Wireless Fading Channels." IEEE Transactions on Wireless Communications 19, no. 5 (May 2020): 3546–57. http://dx.doi.org/10.1109/twc.2020.2974748.

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Yang, Xiaohui, and Zijian Dong. "Kalman Filter-Based Differential Privacy Federated Learning Method." Applied Sciences 12, no. 15 (August 2, 2022): 7787. http://dx.doi.org/10.3390/app12157787.

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The data privacy leakage problem of federated learning has attracted widespread attention. Using differential privacy can protect the data privacy of each node in the federated learning, but adding noise to the model parameters will reduce the accuracy and convergence efficiency of the model. A Kalman Filter-based Differential Privacy Federated Learning Method (KDP-FL) has been proposed to solve this problem, which reduces the impact of the noise added on the model by Kalman filtering. Furthermore, the effectiveness of the proposed method is verified in the case of both Non-IID and IID data distributions. The experiments show that the accuracy of the proposed method is improved by 0.3–4.5% compared to differential privacy federated learning.

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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.

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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.

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Liu, Yejia, Weiyuan Wu, Lampros Flokas, Jiannan Wang, and Eugene Wu. "Enabling SQL-based training data debugging for federated learning." Proceedings of the VLDB Endowment 15, no. 3 (November 2021): 388–400. http://dx.doi.org/10.14778/3494124.3494125.

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How can we debug a logistic regression model in a federated learning setting when seeing the model behave unexpectedly (e.g., the model rejects all high-income customers' loan applications)? The SQL-based training data debugging framework has proved effective to fix this kind of issue in a non-federated learning setting. Given an unexpected query result over model predictions, this framework automatically removes the label errors from training data such that the unexpected behavior disappears in the retrained model. In this paper, we enable this powerful framework for federated learning. The key challenge is how to develop a security protocol for federated debugging which is proved to be secure, efficient, and accurate. Achieving this goal requires us to investigate how to seamlessly integrate the techniques from multiple fields (Databases, Machine Learning, and Cybersecurity). We first propose FedRain, which extends Rain, the state-of-the-art SQL-based training data debugging framework, to our federated learning setting. We address several technical challenges to make FedRain work and analyze its security guarantee and time complexity. The analysis results show that FedRain falls short in terms of both efficiency and security. To overcome these limitations, we redesign our security protocol and propose Frog, a novel SQL-based training data debugging framework tailored for federated learning. Our theoretical analysis shows that Frog is more secure, more accurate, and more efficient than FedRain. We conduct extensive experiments using several real-world datasets and a case study. The experimental results are consistent with our theoretical analysis and validate the effectiveness of Frog in practice.

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Oh, Seungeun, Jihong Park, Eunjeong Jeong, Hyesung Kim, Mehdi Bennis, and Seong-Lyun Kim. "Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup." IEEE Communications Letters 24, no. 10 (October 2020): 2211–15. http://dx.doi.org/10.1109/lcomm.2020.3003693.

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Elbir, Ahmet M., Anastasios K. Papazafeiropoulos, and Symeon Chatzinotas. "Federated Learning for Physical Layer Design." IEEE Communications Magazine 59, no. 11 (November 2021): 81–87. http://dx.doi.org/10.1109/mcom.101.2100138.

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Chen, Mingzhe, H. Vincent Poor, Walid Saad, and Shuguang Cui. "Wireless Communications for Collaborative Federated Learning." IEEE Communications Magazine 58, no. 12 (December 2020): 48–54. http://dx.doi.org/10.1109/mcom.001.2000397.

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Zhan, Yufeng, Peng Li, Zhihao Qu, Deze Zeng, and Song Guo. "A Learning-Based Incentive Mechanism for Federated Learning." IEEE Internet of Things Journal 7, no. 7 (July 2020): 6360–68. http://dx.doi.org/10.1109/jiot.2020.2967772.

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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.

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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.

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Rincon, Jaime, Vicente Julian, and Carlos Carrascosa. "FLaMAS: Federated Learning Based on a SPADE MAS." Applied Sciences 12, no. 7 (April 6, 2022): 3701. http://dx.doi.org/10.3390/app12073701.

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In recent years federated learning has emerged as a new paradigm for training machine learning models oriented to distributed systems. The main idea is that each node of a distributed system independently trains a model and shares only model parameters, such as weights, and does not share the training data set, which favors aspects such as security and privacy. Subsequently, and in a centralized way, a collective model is built that gathers all the information provided by all of the participating nodes. Several federated learning framework proposals have been developed that seek to optimize any aspect of the learning process. However, a lack of flexibility and dynamism is evident in many cases. In this regard, this study aims to provide flexibility and dynamism to the federated learning process. The methodology used consists of designing a multi-agent system that can form a federated learning framework where the agents act as nodes that can be easily added to the system dynamically. The proposal has been evaluated with different experiments on the SPADE platform; the results obtained demonstrate the benefits of the federated system while facilitating flexibility and scalability.

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Nasiri, Sara, Iman Nasiri, and Kristof Van Laerhoven. "Wearable xAI: A Knowledge-Based Federated Learning Framework." Engineering Proceedings 6, no. 1 (May 17, 2021): 79. http://dx.doi.org/10.3390/i3s2021dresden-10143.

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Federated learning is a knowledge transmission and training process that occurs in turn between user models on edge devices and the training model in the central server. Due to privacy policies and concerns and heterogeneous data, this is a widespread requirement in federated learning applications. In this work, we use knowledge-based methods, and in particular case-based reasoning (CBR), to develop a wearable, explainable artificial intelligence (xAI) framework. CBR is a problem-solving AI approach for knowledge representation and manipulation, which considers successful solutions of past conditions that are likely to serve as candidate solutions for a requested problem. It enables federated learning when each user owns not only his/her private data, but also uniquely designed cases. New generated cases can be compared to the knowledge base and the recommendations enable the user to communicate better with the whole system. It improves users’ task performance and increases user acceptability when they need explanations to understand why and how AI algorithms arrive at these optimal solutions.

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Chen, Hao, Ming Xiao, and Zhibo Pang. "Satellite-Based Computing Networks with Federated Learning." IEEE Wireless Communications 29, no. 1 (February 2022): 78–84. http://dx.doi.org/10.1109/mwc.008.00353.

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Yang, Qiang, Yongxin Tong, Yang Liu, Yangqiu Song, Hao Peng, and Boi Faltings. "Preface to Federated Learning: Algorithms, Systems, and Applications: Part 2." ACM Transactions on Intelligent Systems and Technology 13, no. 5 (October 31, 2022): 1–2. http://dx.doi.org/10.1145/3536420.

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Crowson, Matthew G., Dana Moukheiber, Aldo Robles Arévalo, Barbara D. Lam, Sreekar Mantena, Aakanksha Rana, Deborah Goss, David W. Bates, and Leo Anthony Celi. "A systematic review of federated learning applications for biomedical data." PLOS Digital Health 1, no. 5 (May 19, 2022): e0000033. http://dx.doi.org/10.1371/journal.pdig.0000033.

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Objectives Federated learning (FL) allows multiple institutions to collaboratively develop a machine learning algorithm without sharing their data. Organizations instead share model parameters only, allowing them to benefit from a model built with a larger dataset while maintaining the privacy of their own data. We conducted a systematic review to evaluate the current state of FL in healthcare and discuss the limitations and promise of this technology. Methods We conducted a literature search using PRISMA guidelines. At least two reviewers assessed each study for eligibility and extracted a predetermined set of data. The quality of each study was determined using the TRIPOD guideline and PROBAST tool. Results 13 studies were included in the full systematic review. Most were in the field of oncology (6 of 13; 46.1%), followed by radiology (5 of 13; 38.5%). The majority evaluated imaging results, performed a binary classification prediction task via offline learning (n = 12; 92.3%), and used a centralized topology, aggregation server workflow (n = 10; 76.9%). Most studies were compliant with the major reporting requirements of the TRIPOD guidelines. In all, 6 of 13 (46.2%) of studies were judged at high risk of bias using the PROBAST tool and only 5 studies used publicly available data. Conclusion Federated learning is a growing field in machine learning with many promising uses in healthcare. Few studies have been published to date. Our evaluation found that investigators can do more to address the risk of bias and increase transparency by adding steps for data homogeneity or sharing required metadata and code.

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Xiao, Bin, Qingzhen Xu, Chengying He, and Jianwu Lin. "Blockchain and Federated Learning Based Bidding Applications in Power Markets." Procedia Computer Science 202 (2022): 21–26. http://dx.doi.org/10.1016/j.procs.2022.04.004.

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Guberović, Emanuel, Charalampos Alexopoulos, Ivana Bosnić, and Igor Čavrak. "Framework for Federated Learning Open Models in e-Government Applications." Interdisciplinary Description of Complex Systems 20, no. 2 (April 28, 2022): 162–78. http://dx.doi.org/10.7906/indecs.20.2.8.

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Rahman, K. M. Jawadur, Faisal Ahmed, Nazma Akhter, Mohammad Hasan, Ruhul Amin, Kazi Ehsan Aziz, A. K. M. Muzahidul Islam, Md Saddam Hossain Mukta, and A. K. M. Najmul Islam. "Challenges, Applications and Design Aspects of Federated Learning: A Survey." IEEE Access 9 (2021): 124682–700. http://dx.doi.org/10.1109/access.2021.3111118.

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Aledhari, Mohammed, Rehma Razzak, Reza M. Parizi, and Fahad Saeed. "Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications." IEEE Access 8 (2020): 140699–725. http://dx.doi.org/10.1109/access.2020.3013541.

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Otoum, Yazan, Vinay Chamola, and Amiya Nayak. "Federated and Transfer Learning-Empowered Intrusion Detection for IoT Applications." IEEE Internet of Things Magazine 5, no. 3 (September 2022): 50–54. http://dx.doi.org/10.1109/iotm.001.2200048.

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Victor, Nancy, Rajeswari Chengoden, Mamoun Alazab, Sweta Bhattacharya, Sindri Magnusson, Praveen Kumar Reddy Maddikunta, Kadiyala Ramana, and Thippa Reddy Gadekallu. "Federated Learning for IoUT: Concepts, Applications, Challenges and Future Directions." IEEE Internet of Things Magazine 5, no. 4 (December 2022): 36–41. http://dx.doi.org/10.1109/iotm.001.2200067.

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Abreha, Haftay Gebreslasie, Mohammad Hayajneh, and Mohamed Adel Serhani. "Federated Learning in Edge Computing: A Systematic Survey." Sensors 22, no. 2 (January 7, 2022): 450. http://dx.doi.org/10.3390/s22020450.

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Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services closer to data sources. EC combined with Deep Learning (DL) is a promising technology and is widely used in several applications. However, in conventional DL architectures with EC enabled, data producers must frequently send and share data with third parties, edge or cloud servers, to train their models. This architecture is often impractical due to the high bandwidth requirements, legalization, and privacy vulnerabilities. The Federated Learning (FL) concept has recently emerged as a promising solution for mitigating the problems of unwanted bandwidth loss, data privacy, and legalization. FL can co-train models across distributed clients, such as mobile phones, automobiles, hospitals, and more, through a centralized server, while maintaining data localization. FL can therefore be viewed as a stimulating factor in the EC paradigm as it enables collaborative learning and model optimization. Although the existing surveys have taken into account applications of FL in EC environments, there has not been any systematic survey discussing FL implementation and challenges in the EC paradigm. This paper aims to provide a systematic survey of the literature on the implementation of FL in EC environments with a taxonomy to identify advanced solutions and other open problems. In this survey, we review the fundamentals of EC and FL, then we review the existing related works in FL in EC. Furthermore, we describe the protocols, architecture, framework, and hardware requirements for FL implementation in the EC environment. Moreover, we discuss the applications, challenges, and related existing solutions in the edge FL. Finally, we detail two relevant case studies of applying FL in EC, and we identify open issues and potential directions for future research. We believe this survey will help researchers better understand the connection between FL and EC enabling technologies and concepts.

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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.

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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.

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Journal articles on the topic 'Federated learning applications'

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