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Personalized federated learning (PFL) is a popular framework that allowsclients to have different models to address application scenarios whereclients' data are in different domains. The typical model of a client in PFLfeatures a global encoder trained by all clients to extract universal featuresfrom the raw data and personalized layers (e.g., a classifier) trained usingthe client's local data. Nonetheless, due to the differences between the datadistributions of different clients (aka, domain gaps), the universal featuresproduced by the global encoder largely encompass numerous components irrelevantto a certain client's local task. Some recent PFL methods address the aboveproblem by personalizing specific parameters within the encoder. However, thesemethods encounter substantial challenges attributed to the high dimensionalityand non-linearity of neural network parameter space. In contrast, the featurespace exhibits a lower dimensionality, providing greater intuitiveness andinterpretability as compared to the parameter space. To this end, we propose anovel PFL framework named FedPick. FedPick achieves PFL in the low-dimensionalfeature space by selecting task-relevant features adaptively for each clientfrom the features generated by the global encoder based on its local datadistribution. It presents a more accessible and interpretable implementation ofPFL compared to those methods working in the parameter space. Extensiveexperimental results show that FedPick could effectively select task-relevantfeatures for each client and improve model performance in cross-domain FL.

Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space