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Peter Richtárik

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#machine learning Preprint Jul 2026

SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity

This work revisits client drift from a novel frequency-domain perspective and uncovers 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 representing fine-grained features remain relatively consistent.

Liyang Yuan, Yibo Yang, Dandan Guo et al. · 0 citations