SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity
Federated Learning (FL) is fundamentally challenged by statistical heterogeneity, where non-identically distributed (non-IID) data induces client drift that severely hampers global convergence. While existing approaches attempt to mitigate this drift through spatial-domain gradient correction or regularization, they overlook the intrinsic spectral structure of optimization signals. In this work, we revisit client drift from a novel frequency-domain perspective and uncover a critical Spectral Bias of Drift: inter-client gradient divergence is predominantly concentrated in low-frequency components which encode client-specific distributional shifts, while high-frequency components representi...
| Authors | Liyang Yuan, Yibo Yang, Dandan Guo et al. |
|---|---|
| Primary category | cs.LG |
| Published | 5 July 2026 |
| Last updated at source | 28 August 2026 |
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