Personalized federated learning (pFL) enables collaborative training among
multiple clients to enhance the capability of customized local models. In pFL,
clients may have heterogeneous (also known as non-IID) data, which poses a key
challenge in how to decouple the data knowledge into generic knowledge for
global sharing and personalized knowledge for preserving local personalization.
A typical way of pFL focuses on label distribution skew, and they adopt a
decoupling scheme where the model is split into a common feature extractor and
two prediction heads (generic and personalized). However, such a decoupling
scheme cannot solve the essential problem of feature skew heterogeneity,
because a common feature extractor cannot decouple the generic and personalized
features. Therefore, in this paper, we rethink the architecture decoupling
design for feature-skew pFL and propose an effective pFL method called FediOS.
In FediOS, we reformulate the decoupling into two feature extractors (generic
and personalized) and one shared prediction head. Orthogonal projections are
used for clients to map the generic features into one common subspace and
scatter the personalized features into different subspaces to achieve
decoupling for them. In addition, a shared prediction head is trained to
balance the importance of generic and personalized features during inference.
Extensive experiments on four vision datasets demonstrate our method reaches
state-of-the-art pFL performances under feature skew heterogeneity.

个性化联邦学习（pFL）通过协同训练多个客户端来增强定制本地模型的能力。本文提出了一种名为 FediOS 的有效 pFL 方法，通过重新设计特征偏斜 pFL 的架构解耦设计，将模型分为两个特征提取器（通用和个性化）和一个共享预测头。通过正交投影将通用特征映射到一个公共子空间，并将个性化特征分散到不同的子空间以实现解耦。此外，在推理过程中，还训练了一个共享预测头来平衡通用特征和个性化特征的重要性。四个视觉数据集上的广泛实验证明了我们的方法在特征偏斜异质性下达到了最先进的 pFL 性能。