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Tackling the Non-IID Issue in Heterogeneous Federated Learning by Gradient Harmonization
March 8, 2024, 5:42 a.m. | Xinyu Zhang, Weiyu Sun, Ying Chen
cs.LG updates on arXiv.org arxiv.org
Abstract: Federated learning (FL) is a privacy-preserving paradigm for collaboratively training a global model from decentralized clients. However, the performance of FL is hindered by non-independent and identically distributed (non-IID) data and device heterogeneity. In this work, we revisit this key challenge through the lens of gradient conflicts on the server side. Specifically, we first investigate the gradient conflict phenomenon among multiple clients and reveal that stronger heterogeneity leads to more severe gradient conflicts. To tackle …
abstract arxiv challenge cs.ai cs.lg data decentralized distributed federated learning global gradient however independent issue key paradigm performance privacy through training type work
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