Asy-MSFL: Communication-Efficient Federated Learning With Multiserver Adaptive Updates

Federated Learning (FL) facilitates collaborative model training across distributed data sources while preserving data privacy, yet traditional FL frameworks often encounter severe challenges in communication efficiency and stable model convergence, especially in large-scale, heterogeneous settings. To address these limitations, we propose Asynchronous Multi-Server Federated Learning (Asy-MSFL), a novel FL framework designed to address communication bottlenecks and improve model convergence in large-scale heterogeneous environments. Unlike traditional client-based layered FL architectures, Asy-MSFL adopts layer-specific management, where each server processes updates from specific layers of client models, reducing communication latency and bandwidth consumption. To effectively manage asynchronous updates and client heterogeneity, Asy-MSFL incorporates a dynamic Elastic Averaging Stochastic Gradient Descent (EASGD) mechanism. By dynamically adjusting the elastic force coefficient, it aligns local updates with global objectives, ensuring consistent and stable convergence despite varying client conditions. Experimental results demonstrate that Asy-MSFL achieves substantial communication cost reductions and high model accuracy, making it an advanced solution for scalable, efficient federated learning with reliable convergence.