Leveraging heuristic client selection for enhanced secure federated submodel learning |
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| Institution: | 1. School of Information Management, Nanjing University, Nanjing 210023, China;2. School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai 200230, China;1. Studio Galilei Co. Ltd., Yongin, Gyeonggi, Republic of Korea;2. Department of Transportation Engineering, College of Engineering, Myongji University, Yongin, Gyeonggi, Republic of Korea;3. Department of Geography, College of Sciences, Kyung Hee University, Seoul, Republic of Korea;4. Department of Transportation Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea;5. Smart Tourism Education Platform, College of Hotel & Tourism Management, Kyung Hee University, Seoul, Republic of Korea;6. Korea Railroad Research Institute, Uiwang, Gyeonggi, Republic of Korea;1. Faculty of Economics and Management, East China Normal University, Shanghai, China;2. Key Laboratory of Advanced Theory and Application in Statistics and Data Science (East China Normal University), Ministry of Education of China, Shanghai, China;3. University of Chinese Academy of Sciences, Shanghai, China;1. School of Information, Renmin University of China, Beijing 100872, PR China;2. School of Information Technology and Management, University of International Business and Economics, Beijing 100029, PR China |
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| Abstract: | As the number of clients for federated learning (FL) has expanded to the billion level, a new research branch named secure federated submodel learning (SFSL) has emerged. In SFSL, mobile clients only download a tiny ratio of the global model from the coordinator’s global. However, SFSL provides little guarantees on the convergence and accuracy performance as the covered items may be highly biased. In this work, we formulate the problem of client selection through optimizing unbiased coverage of item index set for enhancing SFSL performance. We analyze the NP-hardness of this problem and propose a novel heuristic multi-group client selection framework by jointly optimizing index diversity and similarity. Specifically, heuristic exploration on some random client groups are performed progressively for an empirical approximate solution. Meanwhile, private set operations are used to preserve the privacy of participated clients. We implement the proposal by simulating large-scale SFSL application in a lab environment and conduct evaluations on two real-world data-sets. The results demonstrate the performance (w.r.t., accuracy and convergence speed) superiority of our selection algorithm than SFSL. The proposal is also shown to yield significant computation advantage with similar communication performance as SFSL. |
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| Keywords: | Federated learning Billion-scale deep learning Recommendation systems Private set union Maximum set cover |
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