Client Selection for Wireless Federated Learning With Data and Latency Heterogeneity

Federated learning is a distributed machine learning paradigm that allows multiple edge devices to collaboratively train a shared model without exchanging raw data. However, the training efficiency of federated learning is highly dependent on client selection. Moreover, due to the varying wireless communication environments and various computation latencies among clients, selecting clients randomly or uniformly may not be optimal for balancing data diversity and training efficiency. In this paper, we formulate a new latency-minimization problem that simultaneously optimizes client selection and training procedures in federated learning, which takes into account the data and latency heterogeneity among clients. Given the non-convexity of the problem, we derive a new convergence upper bound for federated learning with probabilistic client selection. To solve the mixed integer nonlinear programming problem, we introduce a hybrid solution that integrates grid search techniques with the polyhedral active set algorithm. Numerical analyses and experiments on real-world data demonstrate that our scheme outperforms existing ones in terms of overall training latency and achieves up to 3 times acceleration over random client selection, especially in scenarios with highly heterogeneous data and latencies among clients.