Federated learning (FL) faces challenges of intermittent client availability and computation/
communication efficiency. As a result, only a small subset of clients can participate in FL at a …

Federated Learning (FL) has emerged as a de facto machine learning area and received
rapid increasing research interests from the community. However, catastrophic forgetting …

This paper proposes a vehicular edge federated learning (VEFL) solution, where an edge
server leverages highly mobile connected vehicles'(CVs') onboard central processing units …

Availability of high-quality large annotated data is indeed a significant challenge in
healthcare. In addition, privacy concerns and data-sharing restrictions often hinder access to …

We propose a novel framework to study asynchronous federated learning optimization with
delays in gradient updates. Our theoretical framework extends the standard FedAvg …

Federated min-max learning has received increasing attention in recent years thanks to its
wide range of applications in various learning paradigms. Similar to the conventional …

This paper advocates a new paradigm Personalized Empirical Risk Minimization (PERM) to
facilitate learning from heterogeneous data sources without imposing stringent constraints …

Minimax optimization has seen a surge in interest with the advent of modern applications
such as GANs, and it is inherently more challenging than simple minimization. The difficulty …

Federated Learning (FL) is an efficient distributed machine learning paradigm that employs
private datasets in a privacy-preserving manner. The main challenges of FL are that end …

Federated Learning (FL) is a distributed machine learning approach where multiple clients
work together to solve a machine learning task. One of the key challenges in FL is the issue …