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 …

The ultra-low latency requirements of 5G/6G applications and privacy constraints call for
distributed machine learning systems to be deployed at the edge. With its simple yet …

This paper focuses on addressing the practical yet challenging problem of model
heterogeneity in federated learning, where clients possess models with different network …

Personalized federated learning with differential privacy has been considered a feasible
solution to address non-IID distribution of data and privacy leakage risks. However, current …

An oft-cited challenge of federated learning is the presence of heterogeneity.\emph {Data
heterogeneity} refers to the fact that data from different clients may follow very different …

Federated Learning (FL) is popular for its privacy-preserving and collaborative learning
capabilities. Recently, personalized FL (pFL) has received attention for its ability to address …

Federated learning (FL) is an emerging machine learning paradigm that allows multiple
parties to train a shared model collaboratively in a privacy-preserving manner. Existing …

Federated learning (FL) is an emerging machine learning paradigm in which a central
server coordinates multiple participants (clients) collaboratively to train on decentralized …

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of
the world's web traffic, making a great data source for various machine learning (ML) …

One of the crucial issues in federated learning is how to develop efficient optimization
algorithms. Most of the current ones require full device participation and/or impose strong …