SignSGD Algorithm

SignSGD is a communication-efficient distributed optimization algorithm that quantizes gradients to a single bit (sign), significantly reducing communication overhead in training large deep learning models across multiple devices. Current research focuses on improving SignSGD's robustness and convergence, particularly addressing challenges posed by heterogeneous computational resources and the presence of faulty or malicious nodes, through techniques like federated voting and sparse gradient selection. These advancements enhance the practicality and scalability of distributed training, impacting various applications by enabling faster and more resilient model development in resource-constrained environments.