  Personalized federated learning (PFL) is a popular framework that allows
clients to have different models to address application scenarios where
clients' data are in different domains. The typical model of a client in PFL
features a global encoder trained by all clients to extract universal features
from the raw data and personalized layers (e.g., a classifier) trained using
the client's local data. Nonetheless, due to the differences between the data
distributions of different clients (aka, domain gaps), the universal features
produced by the global encoder largely encompass numerous components irrelevant
to a certain client's local task. Some recent PFL methods address the above
problem by personalizing specific parameters within the encoder. However, these
methods encounter substantial challenges attributed to the high dimensionality
and non-linearity of neural network parameter space. In contrast, the feature
space exhibits a lower dimensionality, providing greater intuitiveness and
interpretability as compared to the parameter space. To this end, we propose a
novel PFL framework named FedPick. FedPick achieves PFL in the low-dimensional
feature space by selecting task-relevant features adaptively for each client
from the features generated by the global encoder based on its local data
distribution. It presents a more accessible and interpretable implementation of
PFL compared to those methods working in the parameter space. Extensive
experimental results show that FedPick could effectively select task-relevant
features for each client and improve model performance in cross-domain FL.
