Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee

ArXi:2605.28335v1 Announce Type: new 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