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 …

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 …

Federated learning (FL) allows multiple clients to collectively train a high-performance
global model without sharing their private data. However, the key challenge in federated …

Abstract Personalized Federated Learning (PFL) represents a promising solution for
decentralized learning in heterogeneous data environments. Partial model personalization …

Federated learning (FL) enables multiple clients to jointly train high-performance deep
learning models while maintaining the training data locally. However, it is challenging to …

Data heterogeneity is one of the most challenging issues in federated learning, which
motivates a variety of approaches to learn personalized models for participating clients. One …

Federated learning enables multiple parties to collaboratively train a machine learning
model without communicating their local data. A key challenge in federated learning is to …

Pre-training is prevalent in nowadays deep learning to improve the learned model's
performance. However, in the literature on federated learning (FL), neural networks are …

Federated learning is a distributed paradigm that allows multiple parties to collaboratively
train deep models without exchanging the raw data. However, the data distribution among …

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 …