Byzantine-Resilient Federated Learning at Edge

• URL: http://arxiv.org/abs/2303.10434v1
• Date: Sat, 18 Mar 2023 15:14:16 GMT
• Title: Byzantine-Resilient Federated Learning at Edge
• Authors: Youming Tao, Sijia Cui, Wenlu Xu, Haofei Yin, Dongxiao Yu, Weifa Liang, Xiuzhen Cheng
• Abstract summary: We present a Byzantine-resilient descent algorithm that can handle heavy-tailed data. We also propose an algorithm that incorporates costs during the learning process.
• Score: 20.742023657098525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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