Asynchronous Algorithm

Asynchronous algorithms are designed to accelerate computations by allowing parallel processing of tasks without strict synchronization, addressing limitations of synchronous methods in large-scale distributed systems. Current research focuses on improving the efficiency and convergence of asynchronous algorithms in various contexts, including federated learning, distributed optimization, and reinforcement learning, often employing techniques like resource-adaptive scheduling, gradient tracking, and delay-adaptive step sizes. These advancements are significant for tackling the challenges of heterogeneity and communication delays in large-scale machine learning, leading to faster training times and improved model performance in diverse applications.