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 is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

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

In parallel with the rapid adoption of artificial intelligence (AI) empowered by advances in AI
research, there has been growing awareness and concerns of data privacy. Recent …

The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

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 …

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

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 …

Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon
decentralized data and training that brings learning to the edge or directly on-device. FL is a …