用振荡器同步机制改进联邦学习收敛速度
Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity
- 将客户端更新视为振荡器，按相位对齐程度动态加权
- 在多个数据集上收敛速度提升，准确率显著提高
- 适合处理非独立同分布数据的联邦学习场景
在非独立同分布（non-IID）客户端数据下，联邦学习因客户端漂移导致收敛缓慢。本文提出Kuramoto-FedAvg，将权重聚合重定义为受基顿模型启发的同步问题。服务器根据各客户端更新与全局更新的相位对齐程度动态加权，强化与全局梯度方向一致的贡献，抑制相位偏离的更新影响。理论证明该同步机制可减少客户端漂移，相比标准FedAvg在异构数据分布下提供更紧的收敛界。实证结果支持理论发现，在多个基准数据集上均显著加速收敛并提升精度。本工作揭示了基于协调与同步策略在管理梯度多样性、加速真实非IID联邦优化中的潜力。
原文摘要 · Abstract (English)
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.
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