Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …

Abstract Personalized Federated Learning (pFL) not only can capture the common priors
from broad range of distributed data, but also support customized models for heterogeneous …

Heterogeneity across clients in federated learning (FL) usually hinders the optimization
convergence and generalization performance when the aggregation of clients' knowledge …

Personalized federated learning is proposed to handle the data heterogeneity problem
amongst clients by learning dedicated tailored local models for each user. However, existing …

We consider two federated learning algorithms for training partially personalized models,
where the shared and personal parameters are updated either simultaneously or alternately …

Federated learning is promising for its capability to collaboratively train models with multiple
clients without accessing their data, but vulnerable when clients' data distributions diverge …

A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the
generalization of the global model on each client. To address this, we propose a method …

Federated learning has evolved to improve a single global model under data heterogeneity
(as a curse) or to develop multiple personalized models using data heterogeneity (as a …