Asynchronous SGD

Asynchronous Stochastic Gradient Descent (ASGD) aims to accelerate distributed machine learning by allowing parallel workers to update model parameters independently, without synchronization delays. Current research focuses on improving ASGD's efficiency and convergence guarantees in heterogeneous environments with varying worker compute and communication times, developing novel algorithms like MindFlayer SGD, Freya PAGE, and Shadowheart SGD that address this challenge. These advancements are significant because they enable faster training of large-scale models, particularly in decentralized or federated learning settings where resource heterogeneity is common, ultimately impacting the scalability and practicality of many machine learning applications.