Centralized Stochastic Gradient Descent

Centralized Stochastic Gradient Descent (SGD) is a foundational machine learning optimization technique aiming to efficiently minimize loss functions by iteratively updating model parameters using stochastic gradient estimates. Current research heavily focuses on extending centralized SGD to distributed and decentralized settings, exploring algorithms like gossip-based methods and momentum-based approaches to address challenges posed by communication delays, heterogeneous data distributions, and limited communication bandwidth. These advancements are crucial for scaling machine learning to massive datasets and enabling privacy-preserving collaborative learning, with significant implications for both theoretical understanding of optimization and practical applications in areas like federated learning.