SignSGD MV

SignSGD MV (majority voting) is a distributed optimization algorithm designed to reduce communication overhead in large-scale machine learning by transmitting only the sign of gradients, rather than the full gradient vectors. Current research focuses on improving its robustness and convergence, particularly in heterogeneous environments with varying worker computational capabilities and the presence of adversarial or faulty nodes. This involves developing techniques like federated voting and magnitude-aware sparsification to address issues like data heterogeneity and Byzantine attacks, ultimately aiming for faster and more reliable distributed training. The resulting improvements in efficiency and resilience have significant implications for federated learning and other distributed training scenarios.