Classical federated learning (FL) enables training machine learning models without sharing data for privacy preservation, but heterogeneous data characteristic degrades the performance of the localized model. Personalized FL (PFL) addresses this by synthesizing personalized models from a global model via training on local data. Such a global model may overlook the specific information that the clients have been sampled. In this paper, we propose a novel scheme to inject personalized prior knowledge into the global model in each client, which attempts to mitigate the introduced incomplete information problem in PFL. At the heart of our proposed approach is a framework, the PFL with Bregman Divergence (pFedBreD), decoupling the personalized prior from the local objective function regularized by Bregman divergence for greater adaptability in personalized scenarios. We also relax the mirror descent (RMD) to extract the prior explicitly to provide optional strategies. Additionally, our pFedBreD is backed up by a convergence analysis. Sufficient experiments demonstrate that our method reaches the state-of-the-art performances on 5 datasets and outperforms other methods by up to 3.5% across 8 benchmarks. Extensive analyses verify the robustness and necessity of proposed designs.

翻译：经典联邦学习（FL）通过不共享数据实现隐私保护的机器学习模型训练，但异构数据特征会降低本地化模型的性能。个性化联邦学习（PFL）通过基于本地数据训练，从全局模型合成个性化模型来解决此问题。然而，此类全局模型可能忽略客户端已采样的特定信息。本文提出一种新颖方案，为每个客户端的全局模型注入个性化先验知识，旨在缓解PFL中引入的不完整信息问题。我们方法的核心是结合布雷格曼散度的PFL框架（pFedBreD），将个性化先验与通过布雷格曼散度正则化的局部目标函数解耦，以增强个性化场景的适应性。我们还松弛了镜像下降（RMD）以显式提取先验，提供可选策略。此外，我们的pFedBreD通过收敛性分析提供理论支持。充分的实验表明，我们的方法在5个数据集上达到当前最优性能，并在8个基准测试中性能优于其他方法最多3.5%。广泛分析验证了所提设计的鲁棒性与必要性。