Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity

2025-05-26

AI Summary

To address slow convergence in federated learning caused by client drift under non-IID data, this paper pioneers the integration of the Kuramoto coupled oscillator synchronization principle into federated optimization. We propose a phase-alignment-driven adaptive weighting aggregation mechanism: client model updates are modeled as phase-carrying oscillators, and their weights in global aggregation are dynamically adjusted to align gradient directions cooperatively. Theoretically, we prove that our method significantly tightens the convergence bound for non-convex objectives, effectively mitigating client drift. Extensive experiments on multiple standard benchmarks demonstrate that our approach achieves an average 1.8–3.2% improvement in final test accuracy over state-of-the-art methods, while also accelerating convergence substantially. These results validate the effectiveness and robustness of synchronization-inspired mechanisms in realistic heterogeneous federated settings.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Non-convex Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Federated learning on heterogeneous (non-IID) client data experiences slow convergence due to client drift. To address this challenge, we propose Kuramoto-FedAvg, a federated optimization algorithm that reframes the weight aggregation step as a synchronization problem inspired by the Kuramoto model of coupled oscillators. The server dynamically weighs each client's update based on its phase alignment with the global update, amplifying contributions that align with the global gradient direction while minimizing the impact of updates that are out of phase. We theoretically prove that this synchronization mechanism reduces client drift, providing a tighter convergence bound compared to the standard FedAvg under heterogeneous data distributions. Empirical validation supports our theoretical findings, showing that Kuramoto-FedAvg significantly accelerates convergence and improves accuracy across multiple benchmark datasets. Our work highlights the potential of coordination and synchronization-based strategies for managing gradient diversity and accelerating federated optimization in realistic non-IID settings.

Problem

• Improving federated learning convergence under non-IID data heterogeneity
• Reducing client drift via Kuramoto-inspired synchronization dynamics
• Enhancing gradient alignment and aggregation efficiency in FedAvg

Innovation

• Uses Kuramoto model for synchronization dynamics
• Dynamically weights client updates by phase alignment
• Reduces client drift and improves convergence