Distributed consensus problem with caching on federated learning framework
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 dor...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-04-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501329221092932 |
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| _version_ | 1849307788107317248 |
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| author | Xin Yan Yiming Qin Xiaodong Hu Xiaoling Xiao |
| author_facet | Xin Yan Yiming Qin Xiaodong Hu Xiaoling Xiao |
| author_sort | Xin Yan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-288c50b160bb4d9aa45af80bafb561ad |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2022-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-288c50b160bb4d9aa45af80bafb561ad2025-08-20T03:54:38ZengWileyInternational Journal of Distributed Sensor Networks1550-14772022-04-011810.1177/15501329221092932Distributed consensus problem with caching on federated learning frameworkXin Yan0Yiming Qin1Xiaodong Hu2Xiaoling Xiao3School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, ChinaSchool of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, ChinaThe Faculty of Education, The University of Hong Kong, Hong Kong, ChinaSchool of Computer Science, Yangtze University, Jingzhou, ChinaFederated 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.https://doi.org/10.1177/15501329221092932 |
| spellingShingle | Xin Yan Yiming Qin Xiaodong Hu Xiaoling Xiao Distributed consensus problem with caching on federated learning framework International Journal of Distributed Sensor Networks |
| title | Distributed consensus problem with caching on federated learning framework |
| title_full | Distributed consensus problem with caching on federated learning framework |
| title_fullStr | Distributed consensus problem with caching on federated learning framework |
| title_full_unstemmed | Distributed consensus problem with caching on federated learning framework |
| title_short | Distributed consensus problem with caching on federated learning framework |
| title_sort | distributed consensus problem with caching on federated learning framework |
| url | https://doi.org/10.1177/15501329221092932 |
| work_keys_str_mv | AT xinyan distributedconsensusproblemwithcachingonfederatedlearningframework AT yimingqin distributedconsensusproblemwithcachingonfederatedlearningframework AT xiaodonghu distributedconsensusproblemwithcachingonfederatedlearningframework AT xiaolingxiao distributedconsensusproblemwithcachingonfederatedlearningframework |