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