Personalized Federated Learning (PerFL) is a new machine learning paradigm
that delivers personalized models for diverse clients under federated learning
settings. Most PerFL methods require extra learning processes on a client to
adapt a globally shared model to the client-specific personalized model using
its own local data. However, the model adaptation process in PerFL is still an
open challenge in the stage of model deployment and test time. This work
tackles the challenge by proposing a novel federated learning framework to
learn only one robust global model to achieve competitive performance to those
personalized models on unseen/test clients in the FL system. Specifically, we
design a new Client-Supervised Federated Learning (FedCS) to unravel clients'
bias on instances' latent representations so that the global model can learn
both client-specific and client-agnostic knowledge. Experimental study shows
that the FedCS can learn a robust FL global model for the changing data
distributions of unseen/test clients. The FedCS's global model can be directly
deployed to the test clients while achieving comparable performance to other
personalized FL methods that require model adaptation.

通过提出一种新的联邦学习框架，使得个性化联邦学习可以学习一个强健的全局模型，以在联邦学习系统中对未知的 / 测试客户端达到与个性化模型相当的性能。