MoE Enhanced Federated Learning for Spatiotemporal Prediction

The paper introduces MoE-FedTP, a personalized federated learning framework utilizing lightweight Mixture-of-Experts (MoE) networks for spatiotemporal traffic prediction. It employs spatiotemporal neural networks and a gating mechanism for dynamic expert fusion, achieving superior performance on four real-world traffic datasets compared to existing cross-city and federated learning methods. This approach addresses privacy concerns while improving prediction accuracy in data-scarce urban environments, making it significant for practitioners focused on enhancing intelligent transportation systems.