Adaptive Central Acceleration With Variance Control for Robust Federated Optimization in Ubiquitous Intelligence

Abstract

Federated learning (FL) in Intelligent Internet of Things (IIoT) environments faces critical challenges, including sparse client participation, non-IID local data distributions, and unreliable communication, which lead to slow convergence and high variance in global updates. To address these issues, we propose adaptive central federated momentum optimization (ACFMO), an optimization framework that enhances FL efficiency and stability under constrained participation. ACFMO integrates an adaptive central acceleration mechanism that dynamically adjusts momentum updates based on real-time client availability, preventing instability and ensuring smoother global model updates. Additionally, a variance-controlled local updating strategy refines client contributions, mitigating high variance caused by infrequent and heterogeneous updates. Extensive experiments across diverse FL scenarios demonstrate that ACFMO significantly accelerates convergence, reduces communication overhead, and improves model stability compared to state-of-the-art FL methods, making it particularly well-suited for real-world IIoT deployments where network and computational resources are constrained.