Mitigating client heterogeneity in federated learning: a reinforcement learning approach for efficient client selection

Federated learning (FL) is a highly promising distributed machine learning method that enables collaborative model training while protecting participants’ data. In Internet of Things (IoT) applications, due to the large number of devices and high communication overhead, selecting a subset of clients