Decentralized Stochastic Gradient Descent

Decentralized Stochastic Gradient Descent (D-SGD) is a distributed optimization technique enabling collaborative model training across multiple devices without a central server, aiming to improve efficiency and scalability in machine learning. Current research focuses on enhancing D-SGD's convergence speed, generalization ability, and robustness to communication constraints and data heterogeneity, exploring various algorithmic improvements and communication topologies. These advancements are significant for large-scale machine learning applications, offering solutions for privacy-preserving training and improved performance in resource-limited environments.