… The goal of traditional federated learning (FL) is to fit a single … , we focus on the personalized
federated learning problem in … thus it is promising to learn personalized model hθi ∈ H : X ↦…

… Federated learning faces huge challenges from model … proposes a novel personalized
federated learning method via Bayesian … A natural question is can we design a federated learning …

Personalized Federated Learning (pFL), which utilizes and deploys distinct local models,
has gained increasing attention in recent years due to its success in handling the statistical …

… clients by learning dedicated tailored … personalized federated learning framework in a
decentralized (peer-to-peer) communication protocol named DisPFL, which employs personalized …

… For the collaborative learning and personalization goals of pFL, we propose GPFL to
simultaneously learn global and personalized information on the client. We show the superiority of …

… Federated learning allows clients to collaboratively learn statistical models while keeping
their data local. Federated learning … , recent personalized federated learning methods train a …

… propose a novel personalized federated learning framework, … contextual heterogeneity
in the federated setting. To the best … (CG) for personalized federated learning and further …

… of personalized models. In this paper, we tackle the challenging personalized federated
learning … relationships to achieve outstanding personalized performance without using a global …

… Federated learning is a new machine learning technique with … It can be viewed as social
learning involving multiple agents … ordinary federated learning, personalized federated learning …

… We focus on the personalized federated classification task. Suppose there exist C clients. …
groups of works: personalized federated learning and federated uncertainty quantification. …