Adaptive Client Model Update with Reinforcement Learning in Synchronous Federated Learning
Zirou Pan, Huan Geng, Linna Wei, Wei Zhao · 2022
Federated learning is widely applied in green wireless communication, mobile technologies and daily life. It allows multiple parties to jointly train a model on their combined data without revealing any of their local data to a centralized server. However, in practical applications, federated learning requires frequent communication between clients and servers, which brings a considerable burden. In this work, we propose a Federated Learning Deep Q-Learning (FL-DQL) method to reduce the communication frequency between clients and servers in federated learning. FL-DQL selects the local-self-update times of a client adaptively and finds the best trade-off between local update and global parameter aggregation. The performance of FL-DQL is evaluated via extensive experiments with real datasets on a networked prototype system. Results show that FL-DQL effectively reduces the communication overhead among the nodes in our experiments which conforms to the green initiative.