A Multi-Agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning

Federated learning (FL) is a training technique that enables client devices to jointly learn shared model by aggregating locally computed models without exposing their raw data. While most of the existing work focuses on improving FL accuracy, in this paper, we focus efficiency, which often hurdle for adopting real world applications. Specifically, design an efficient framework optimizes processing latency and communication all are primary considerations implementation FL. Inspired recent success Multi Agent Reinforcement Learning (MARL) solving complex control problems, present FedMarl, federated relies trained MARL agents perform run-time selection. Experiments show FedMarl can significantly improve accuracy with much lower cost.