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：摘要：经典联邦学习（FL）无需共享数据即可训练机器学习模型以保护隐私，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全局模型来合成个性化模型，从而解决这一问题。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，在每个客户端向全局模型中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。该方案核心是一个基于Bregman散度的PFL框架（pFedBreD），通过解耦个性化先验与受Bregman散度正则化的局部目标函数，从而在个性化场景中实现更强的适应性。我们还松弛了镜像下降（RMD）以显式提取先验，从而提供可选策略。此外，pFedBreD得到了收敛性分析的支持。充分的实验表明，我们的方法在5个数据集上达到最先进性能，并在8个基准测试中超越其他方法高达3.5%。广泛的分析验证了所提设计的鲁棒性和必要性。