Local SGD

Local SGD (stochastic gradient descent) is a distributed optimization algorithm designed to efficiently train machine learning models across multiple devices or clusters, reducing communication overhead compared to traditional centralized methods. Current research focuses on understanding its convergence properties under various data heterogeneity assumptions, exploring its application in large language model training and other complex architectures, and developing strategies to mitigate challenges like stragglers and asynchronous updates. These advancements are significant for scaling machine learning to massive datasets and diverse computing environments, improving both training speed and model performance.