SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding

Distributed learning is an effective approach to accelerate model training using multiple workers. However, substantial communication delays emerge between workers and a parameter server due to massive costs associated with communicating gradients. SignSGD with majority voting (signSGD-MV) is a simple yet effective optimizer that reduces communication costs through one-bit quantization, yet the convergence rates considerably decrease as adversarial workers increase. In this paper, we show that the convergence rate is invariant as the number of adversarial workers increases, provided that …

accelerate adversarial adversarial attacks attacks communication cs.cr cs.lg decoding defense distributed eess.sp federated model training parameter server sign simple training voting workers