Byzantine-Resilient Federated Learning at Edge
文献类型：期刊论文
| 作者 | Tao, Youming4; Cui, Sijia3 ; Xu, Wenlu1; Yin, Haofei4; Yu, Dongxiao4; Liang, Weifa2; Cheng, Xiuzhen4
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| 刊名 | IEEE TRANSACTIONS ON COMPUTERS
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| 出版日期 | 2023-09-01 |
| 卷号 | 72期号:9页码:2600-2614 |
| 关键词 | Byzantine resilience communication efficiency edge intelligent systems federated learning |
| ISSN号 | 0018-9340 |
| DOI | 10.1109/TC.2023.3257510 |
| 通讯作者 | Yu, Dongxiao(dxyu@sdu.edu.cn) |
| 英文摘要 | Both Byzantine resilience and communication efficiency have attracted tremendous attention recently for their significance in edge federated learning. However, most existing algorithms may fail when dealing with real-world irregular data that behaves in a heavy-tailed manner. To address this issue, we study the stochastic convex and non-convex optimization problem for federated learning at edge and show how to handle heavy-tailed data while retaining the Byzantine resilience, communication efficiency and the optimal statistical error rates simultaneously. Specifically, we first present a Byzantine-resilient distributed gradient descent algorithm that can handle the heavy-tailed data and meanwhile converge under the standard assumptions. To reduce the communication overhead, we further propose another algorithm that incorporates gradient compression techniques to save communication costs during the learning process. Theoretical analysis shows that our algorithms achieve order-optimal statistical error rate in presence of Byzantine devices. Finally, we conduct extensive experiments on both synthetic and real-world datasets to verify the efficacy of our algorithms. |
| 资助项目 | National Key Research and Development Program of China[2020YFB1005900] ; National Natural Science Foundation of China (NSFC)[62122042] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001047175700014 |
| 出版者 | IEEE COMPUTER SOC |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China (NSFC) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53981]  |
| 专题 | 数字内容技术与服务研究中心_听觉模型与认知计算 |
| 通讯作者 | Yu, Dongxiao |
| 作者单位 | 1.Univ Calif Los Angeles, Dept Stat, Los Angeles, CA 90095 USA 2.City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China 3.Univ Chinese Acad Sci, Inst Automat, Beijing 101408, Peoples R China 4.Shandong Univ, Sch Comp Sci & Technol, Qingdao 250100, Shandong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tao, Youming,Cui, Sijia,Xu, Wenlu,et al. Byzantine-Resilient Federated Learning at Edge[J]. IEEE TRANSACTIONS ON COMPUTERS,2023,72(9):2600-2614. |
| APA | Tao, Youming.,Cui, Sijia.,Xu, Wenlu.,Yin, Haofei.,Yu, Dongxiao.,...&Cheng, Xiuzhen.(2023).Byzantine-Resilient Federated Learning at Edge.IEEE TRANSACTIONS ON COMPUTERS,72(9),2600-2614. |
| MLA | Tao, Youming,et al."Byzantine-Resilient Federated Learning at Edge".IEEE TRANSACTIONS ON COMPUTERS 72.9(2023):2600-2614. |
入库方式： OAI收割
来源：自动化研究所
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