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 …

Data heterogeneity is an inherent challenge that hinders the performance of federated
learning (FL). Recent studies have identified the biased classifiers of local models as the key …

Data heterogeneity across clients in federated learning (FL) settings is a widely
acknowledged challenge. In response, personalized federated learning (PFL) emerged as a …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Federated learning emerges as a solution to the dilemma of data silos while safeguarding
data privacy, particularly relevant in the consumer electronics sector where user data privacy …

Data privacy and long-tailed distribution are the norms rather than the exception in many
real-world tasks. This paper investigates a federated long-tailed learning (Fed-LT) task in …

Federated Learning aims to learn a global model on the server side that generalizes to all
clients in a privacy-preserving manner, by leveraging the local models from different clients …

Federated learning (FL) is a nascent distributed learning paradigm to train a shared global
model without violating users' privacy. FL has been shown to be vulnerable to various …

Federated learning (FL), training deep models from decentralized data without privacy
leakage, has shown great potential in medical image computing recently. However …

Federated learning (FL) is a prevalent distributed machine learning approach that enables
collaborative training of a global model across multiple devices without sharing local data …