Efficient Serverless Federated Learning With Edge Model Migration on Non-IID Data

Abstract

Federated Learning (FL) has emerged as a transformative distributed learning paradigm, offering a privacy-preserving solution for collaborative model training. However, its conventional cloud-centric architecture suffers from a significant communication bottleneck casused by frequent model transmissions and heterogeneous data, particularly in dynamic mobile computing environments. To address this challenge, we introduce EdgeFLow, a novel, efficient serverless FL framework that fundamentally redesigns the system architecture by replacing the central cloud server with a sequential model migration across edge nodes, while deliberately preserving the cluster-based parallel training topology. EdgeFLow targets an intermediate design point between cloud-centric parallel FL and fully decentralized sequential FL, aiming to retain the convergence stability of intra-cluster parallel aggregation while eliminating the long-distance cloud transmissions that dominate the communication overhead in realistic edge deployments. This paradigm confines both model aggregation and propagation to the edge, forming a structured learning “flow” that gives the framework its name. We provide a theoretical analysis of communication savings using queuing theory, and establish a formal convergence guarantee under non-convex objective functions with non-independent and identically distributed (non-IID) data. Building on these findings, we propose Heterogeneity-Aware EdgeFLow (HA-EdgeFLow), an enhanced algorithm that mitigates the impact of non-IID data through a gradient-based client selection mechanism. Experimental results across various configurations validate the theoretical analysis, demonstrating that our proposed framework significantly reduces communication overhead and latency while achieving comparable or superior model accuracy to traditional baselines. As a system-level architectural innovation for communication-efficient FL, EdgeFLow establishes a foundational framework for future developments in edge network learning systems.