解决联邦学习个性化模型失真问题，提升客户端适应能力
Personalized Federated Learning for Gradient Alignment
- 通过梯度方向对齐减少本地训练方差
- 聚合时重对齐全局模型与客户端个性化方向
- 理论证明支持个性化信息保留，适合异构数据场景
个性化联邦学习（pFL）旨在适应客户端的数据分布，但常无法可靠保留个性化信息。本地训练受有限且异构数据引发的高方差梯度阻碍，而聚合过程进一步扭曲客户端特有的优化方向。为此，我们提出 pFLAlign，一种梯度对齐框架，在本地训练和聚合过程中均保持客户端个性化信息。pFLAlign 包含两个互补机制：一是在客户端侧调整梯度方向以降低优化方差；二是通过将全局模型与每个客户端的个性化方向重新对齐，缓解聚合带来的失真。理论上，我们从 PAC-Bayesian 分析推导出 pFLAlign，揭示了个性化梯度对齐如何保护客户端特定信息。实验与消融研究显示，pFLAlign 稳定提升个性化性能，达到当前最优结果。
原文摘要 · Abstract (English)
Personalized federated learning (pFL) aims to adapt models to client specific data distributions, yet it often fails to reliably preserve personalized information. Local training is hindered by high variance gradients induced by limited and heterogeneous client data, while aggregation further distorts client specific optimization directions. To address these challenges, we propose pFLAlign, a gradient alignment framework to maintain client specific information during both local training and aggregation. pFLAlign consists of two complementary mechanisms: one adapts local gradient directions to reduce variance during client side optimization, and the other mitigates aggregation induced distortion by realigning the global model with each client's personalized direction. Theoretically, we derive pFLAlign from a PAC Bayesian analysis, which reveals how personalized gradient alignment preserves client specific information. Our experiments and ablation studies show that pFLAlign consistently improves personalization performance and training stability, achieving state of the art results.
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