用新相似度度量提升联邦学习中客户端选择多样性与模型性能
PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning
- 引入幂范数余弦相似度捕捉梯度高阶特征，更好应对数据异构
- 在不同数据划分下，模型收敛速度和准确率均优于现有方法
- 结合历史队列实现客户端多样性选择，适合实际部署场景
联邦学习（FL）通过避免数据集中存储，在保护隐私的同时利用多源异构数据。然而，现有方法常忽略远端客户端间梯度的复杂相关性，尤其在数据非独立同分布（non-IID）情况下表现受限。本文提出基于幂范数余弦相似度（PNCS）的新型联邦学习框架，通过捕捉梯度的高阶统计特性，有效缓解非IID数据带来的挑战，提升模型聚合时的收敛速度与精度。此外，设计了一种简单算法，借助选择历史队列实现客户端多样性筛选。在VGG16模型上，于多种数据分区设置下的实验表明，该方法持续优于当前最优基准。
原文摘要 · Abstract (English)
Federated Learning (FL) has emerged as a powerful paradigm for leveraging diverse datasets from multiple sources while preserving data privacy by avoiding centralized storage. However, many existing approaches fail to account for the intricate gradient correlations between remote clients, a limitation that becomes especially problematic in data heterogeneity scenarios. In this work, we propose a novel FL framework utilizing Power-Norm Cosine Similarity (PNCS) to improve client selection for model aggregation. By capturing higher-order gradient moments, PNCS addresses non-IID data challenges, enhancing convergence speed and accuracy. Additionally, we introduce a simple algorithm ensuring diverse client selection through a selection history queue. Experiments with a VGG16 model across varied data partitions demonstrate consistent improvements over state-of-the-art methods.
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