FedFleet: A Hierarchical Federated Learning Framework for Faster Convergence in Heterogeneous IoT Systems

The Internet of Things (IoT) connects numerous heterogeneous devices that collect and generate substantial sensitive data to train intelligent models. To protect the privacy of such data, Federated Learning (FL) provides a privacy-preserving distributed machine learning approach. However, these IoT devices are highly heterogeneous, differing in computing and communication capabilities. During the FL training, these variations may result in significant communication delays and computational inefficiencies. We propose FedFleet, an efficient FL framework with a novel clustering method, to accelerate model training in heterogeneous IoT device scenarios. Unlike the existing FL clustering method, which first determines the cluster header, FedFleet clusters devices into different fleets corresponding to the number of edge servers. Specifically, FedFleet divides devices into multiple fleets based on their performance profiles, including computation and communication time. Devices in the same fleet synchronously aggregate their local model parameters at the edge server and then asynchronously update the global model to the central server. Experiments with various models and datasets show that FedFleet outperforms the state-of-the-art FL algorithm, reducing convergence time by 10.2%-56.4% while achieving the same accuracy rate. It also exhibits minimal performance fluctuations under varying device heterogeneity levels, dropout rates, and network conditions. Moreover, FedFleet demonstrates better scalability for high device heterogeneity environments.