  Recently, personalized federated learning (pFL) has attracted increasing
attention in privacy protection, collaborative learning, and tackling
statistical heterogeneity among clients, e.g., hospitals, mobile smartphones,
etc. Most existing pFL methods focus on exploiting the global information and
personalized information in the client-level model parameters while neglecting
that data is the source of these two kinds of information. To address this, we
propose the Federated Conditional Policy (FedCP) method, which generates a
conditional policy for each sample to separate the global information and
personalized information in its features and then processes them by a global
head and a personalized head, respectively. FedCP is more fine-grained to
consider personalization in a sample-specific manner than existing pFL methods.
Extensive experiments in computer vision and natural language processing
domains show that FedCP outperforms eleven state-of-the-art methods by up to
6.69%. Furthermore, FedCP maintains its superiority when some clients
accidentally drop out, which frequently happens in mobile settings. Our code is
public at https://github.com/TsingZ0/FedCP.
