Asynchronous Training

Asynchronous training in machine learning aims to accelerate distributed model training by eliminating the synchronization bottlenecks inherent in traditional synchronous methods. Current research focuses on developing algorithms like asynchronous stochastic gradient descent (ASGD) and its variants, addressing challenges posed by data heterogeneity, straggler effects, and Byzantine failures through techniques such as dual-delayed updates, meta-aggregation, and intelligent client selection. These advancements significantly improve training efficiency and scalability for large-scale machine learning tasks, particularly in federated learning and decentralized settings, impacting both research methodologies and practical applications like recommendation systems and medical image analysis.