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Mar, 2025
具有受损客户端的稳健非对称异构联邦学习
Robust Asymmetric Heterogeneous Federated Learning with Corrupted Clients
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Xiuwen Fang, Mang Ye, Bo Du
TL;DR
本研究解决了存在模型异构和数据受损客户端的稳健联邦学习任务。提出了一种新颖的稳健非对称异构联邦学习框架(RAHFL)和增强多样性的监督对比学习技术,以提高模型在面对不同数据损坏模式时的韧性和适应性。实验结果表明,该方法在多种复杂的联邦学习环境中具有显著的有效性和稳健性。
Abstract
This paper studies a challenging robust
Federated Learning
task with model heterogeneous and data corrupted clients, where the clients have different local model structures.
Data Corruption
is unavoidable due to
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