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) has been a hot topic in recent years. Ever since it was introduced,
researchers have endeavored to devise FL systems that protect privacy or ensure fair …

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 …

Fairness and robustness are two important concerns for federated learning systems. In this
work, we identify that robustness to data and model poisoning attacks and fairness …

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 …

Personalized federated learning is tasked with training machine learning models for multiple
clients, each with its own data distribution. The goal is to train personalized models …

The increasing size of data generated by smartphones and IoT devices motivated the
development of Federated Learning (FL), a framework for on-device collaborative training of …

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 …

In recent years, personalized federated learning (pFL) has attracted increasing attention for
its potential in dealing with statistical heterogeneity among clients. However, the state-of-the …