Improved Gale–Shapley-Based Hierarchical Federated Learning for IoT Scenarios

Federated learning (FL) allows multiple devices to train a high-performance global model cooperatively without sharing their private data. One of the key challenges in FL for Internet of Things (IoT) scenarios is that the statistical heterogeneity in local data distribution for IoT devices, which will cause the low quality of local models, degrading the performance of the global model. To address this problem, focusing on improving the quality of local models, we propose a match-based hierarchical FL framework (FedAvg-Match), in which two IoT devices with complementary datasets are formed as a training group to reduce the influence of data heterogeneity. By introducing Earth mover’s distance for data distribution, we design an improved Gale-Shapley algorithm with time complexity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(N^{2}\log N)$ </tex-math></inline-formula> for IoT device grouping, which can obtain a stable matching. Experimental results show that, compared to the ungrouped FedAvg algorithm, the proposed FedAvg-Match method significantly improves both the accuracy of the global model and the convergence speed of the training process, while also reducing communication costs.