SGD Style

Stochastic gradient descent (SGD) style optimization remains a cornerstone of deep learning, despite the popularity of adaptive optimizers like Adam. Current research focuses on improving SGD's efficiency and robustness in various contexts, including distributed and federated learning, through techniques like gradient compression, asynchronous updates, and novel sampling schemes. These advancements aim to address challenges such as communication bottlenecks in large-scale training, heterogeneous computing environments, and the need for privacy-preserving mechanisms, ultimately leading to faster and more efficient model training across diverse applications.