A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning
This addresses efficiency hurdles for real-world federated learning adoption, though it is incremental as it builds on existing FL and MARL methods.
The paper tackled the problem of training efficiency in federated learning by jointly optimizing model accuracy, processing latency, and communication efficiency, resulting in significant improvements in accuracy with lower latency and cost.
Federated learning (FL) is a training technique that enables client devices to jointly learn a shared model by aggregating locally-computed models without exposing their raw data. While most of the existing work focuses on improving the FL model accuracy, in this paper, we focus on the improving the training efficiency, which is often a hurdle for adopting FL in real-world applications. Specifically, we design an efficient FL framework which jointly optimizes model accuracy, processing latency and communication efficiency, all of which are primary design considerations for real implementation of FL. Inspired by the recent success of Multi-Agent Reinforcement Learning (MARL) in solving complex control problems, we present \textit{FedMarl}, an MARL-based FL framework which performs efficient run-time client selection. Experiments show that FedMarl can significantly improve model accuracy with much lower processing latency and communication cost.