Decentralized SGD

Decentralized Stochastic Gradient Descent (D-SGD) is a distributed machine learning approach aiming to train large models efficiently and privately across multiple agents without a central server. Current research focuses on improving D-SGD's convergence speed and robustness by optimizing communication topologies, addressing data heterogeneity, and mitigating issues like the "entrapment problem" in random walk algorithms. These advancements are significant because they enable scalable training of complex models on massive datasets while preserving data privacy and reducing communication overhead, impacting various fields from federated learning to Internet of Things applications.