LayerFED: Speeding Up Federated Learning with Model Split

Machine learning is increasingly used in edge devices with limited computational resources for tasks such as face recognition, object detection, and voice recognition. Federated Learning (FL) is a promising approach to train models on multiple edge devices without requiring clients to upload their original data to the server. However, challenges such as redundant local parameters during synchronous aggregation and system heterogeneity can significantly impact training performance. To address these challenges, we propose LayerFED, a novel strategy that leverages model splitting and pipelined communication-computation mode. LayerFED enables partial and full updates by splitting the model, and mitigates communication channel congestion during server aggregation by selectively updating parameters during computation. This reduces the amount of information that needs to be communicated between edge devices and the server. We demonstrate through experiments on benchmark datasets that LayerFED improves training time efficiency and accuracy while maintaining model performance.