Enhancing Edge-Assisted Federated Learning with Asynchronous Aggregation and Cluster Pairing

Federated learning (FL) is widely regarded as highly promising because it enables the collaborative training of high-performance machine learning models among a large number of clients while preserving data privacy by keeping the data local. However, many existing FL frameworks have a two-layered architecture, thus requiring the frequent exchange of large-scale model parameters between clients and remote cloud servers over often unstable networks and resulting in significant communication overhead and latency. To address this issue, we propose to introduce edge servers between the clients and the cloud server to assist in aggregating local models, thus combining asynchronous client-edge model aggregation with synchronous edge-cloud model aggregation. By leveraging the clients' idle time to accelerate training, the proposed framework can achieve faster convergence and reduce the amount of communication traffic. To make full use of the grouping properties inherent in three-layer FL, we propose a similarity matching strategy between edges and clients, thus improving the effect of asynchronous training. We further propose to introduce model-contrastive learning into the loss function and personalize the clients' local models to address the potential learning issues resulting from asynchronous local training in order to further improve the convergence speed. Extensive experiments confirm that our method exhibits significant improvements in model accuracy and convergence speed when compared with other state-of-the-art federated learning architectures.