Federated learning (FL) is a technique that allows multiple clients to collaboratively train a global model without sharing their sensitive and bandwidth-hungry data. This paper presents a joint early client termination and local epoch adjustment for FL. We consider the challenges of heterogeneous Internet of Things (IoT) environments including non-independent and identically distributed (non-IID) data as well as diverse computing and communication capabilities. The goal is to strike the best tradeoff among three conflicting objectives, namely global model accuracy, training latency and communication cost. We first leverage the balanced-MixUp technique to mitigate the influence of non-IID data on the FL convergence rate. A weighted sum optimization problem is then formulated and solved via our proposed FL double deep reinforcement learning (FedDdrl) framework, which outputs a dual action. The former indicates whether a participating FL client is dropped, whereas the latter specifies how long each remaining client needs to complete its local training task. Simulation results show that FedDdrl outperforms the existing FL scheme in terms of overall tradeoff. Specifically, FedDdrl achieves higher model accuracy by about 4% while incurring 30% less latency and communication costs.
Keywords: Internet of Things; client selection; deep reinforcement learning; federated learning; local epoch adjustment.