Communication-efficient Federated Learning with Cooperative Filter Selection

Abstract

Federated learning, as a distributed machine learning framework that a shared global model that is obtained through frequent local training parameter interaction on each participated device. However, the limited communication bandwidth of participating IoT and edge devices will have a conflict between the frequent parameter-interaction learning mode of federated learning and impact communication and learning efficiency. In this paper, a communication efficiency enhanced federated learning technique is presented by proposing a cooperative filter selection method. The Geometric Median of each layer in the global model is adopted as the criterion to cooperatively select important filters in the local model, and then the corresponding parameters interact with other nodes to achieve efficient communication. Experimental results show that our method has a maximum of $2.66\times$ improvements in communication efficiency compared with the state-of-the-art methods.