  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.
