解决单客户端数据多样性的个性化联邦学习问题
Towards Instance-wise Personalized Federated Learning via Semi-Implicit Bayesian Prompt Tuning
- 从贝叶斯视角用隐式分布生成视觉提示，实现细粒度个性化
- 在多个基准数据集上优于现有方法，跨特征与标签异构场景均有效
- 适合多源数据混合的客户端，如医疗、金融等隐私敏感场景
联邦学习（FL）是一种保护隐私的机器学习范式，可在不共享原始数据的前提下实现分布式客户端协同建模。个性化联邦学习（pFL）因其能应对数据异构性而受到关注。然而，现有pFL方法通常假设每个客户端的数据服从单一分布，为每个客户端学习一个个性化模型。这一假设在实际中常不成立，因单个客户端可能包含多个来源或领域的数据，导致显著的客户端内异构性，影响性能。为此，我们提出pFedBayesPT，一种基于视觉提示调优的细粒度实例级个性化联邦学习框架。具体而言，我们将实例级提示生成建模为贝叶斯问题，将提示后验设为隐式分布以捕捉多样化的视觉语义，并在半隐式变分推断框架下推导出变分训练目标。大量实验表明，pFedBayesPT在特征异构和标签异构设置下均持续优于现有pFL方法。
原文摘要 · Abstract (English)
Federated learning (FL) is a privacy-preserving machine learning paradigm that enables collaborative model training across multiple distributed clients without disclosing their raw data. Personalized federated learning (pFL) has gained increasing attention for its ability to address data heterogeneity. However, most existing pFL methods assume that each client's data follows a single distribution and learn one client-level personalized model for each client. This assumption often fails in practice, where a single client may possess data from multiple sources or domains, resulting in significant intra-client heterogeneity and suboptimal performance. To tackle this challenge, we propose pFedBayesPT, a fine-grained instance-wise pFL framework based on visual prompt tuning. Specifically, we formulate instance-wise prompt generation from a Bayesian perspective and model the prompt posterior as an implicit distribution to capture diverse visual semantics. We derive a variational training objective under the semi-implicit variational inference framework. Extensive experiments on benchmark datasets demonstrate that pFedBayesPT consistently outperforms existing pFL methods under both feature and label heterogeneity settings.
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