Federated Trace: A Node Selection Method for More Efficient Federated Learning

Abstract

Federated Learning (FL) is a learning paradigm, which allows the model to directly use a large amount of data in edge devices for training without heavy communication costs and privacy leakage. An important problem that FL faced is the heterogeneity of data at different edge nodes, resulting in a lack of convergence efficiency. In this paper, we propose Federated Trace (FedTrace) to address this problem. In FedTrace, we define the time series of some performance metrics of the global model on the edge node as the training trace of this node, which can reflect the data distribution of the edge node. By clustering the training traces, we can know which nodes have similar data distribution, which can guide the selection of nodes in each round of training. Here, we use a simple but effective method, that is, randomly selecting nodes from each cluster evenly. Experiments on various settings demonstrate that our method significantly reduces the number of communication rounds required in FL.