Asynchronous Communication

Asynchronous communication in distributed systems aims to improve the efficiency and scalability of collaborative tasks by allowing independent agents or nodes to operate and communicate without strict synchronization. Current research focuses on developing algorithms and models, such as asynchronous stochastic gradient descent and federated learning variations, that address challenges like straggling nodes and non-identical data distributions while maintaining convergence guarantees. This research is significant for advancing distributed machine learning, multi-agent systems, and other applications requiring efficient coordination in environments with unpredictable delays or heterogeneous participants, leading to faster training times and improved resource utilization.