Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis
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Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee
arXiv:2605.28335v2 Announce Type: replace Abstract: Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but it is highly vulnerable to Byzantine attacks. Existing robust approaches can neutralize these threats but incur substantial computational overhead during high-dimensional gradient aggregation, an overhead that scales poorly with model size and increasingly dominates the training cost as modern models grow larger. To address this...