Federated learning with heavy-tailed gradient noise and communication noise: a variance-reduction based algorithm
VRA-FedSGD is a variance-reduction algorithm for federated learning designed to handle heavy-tailed gradient noise and communication noise. It uses momentum reduction and nonlinear mappings to stabilize updates, achieving strong convergence rates for both full and partial client participation scenarios.