Over the past few years, significant advancements have been made in the field of machine
learning (ML) to address resource management, interference management, autonomy, and …

In recent years, various machine learning (ML) solutions have been developed to solve
resource management, interference management, autonomy, and decision-making …

Federated learning is generally used in tasks where labels are readily available (eg, next
word prediction). Relaxing this constraint requires design of unsupervised learning …

Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift
in real-world Semantic Segmentation (SS) without compromising the private nature of the …

Federated Learning has become an important learning paradigm due to its privacy and
computational benefits. As the field advances, two key challenges that still remain to be …

Personalized federated learning algorithms have shown promising results in adapting
models to various distribution shifts. However, most of these methods require labeled data …

Federated Learning (FL) has recently emerged as a novel machine learning paradigm
allowing to preserve privacy and to account for the distributed nature of the learning process …

Decentralized learning has been advocated and widely deployed to make efficient use of
distributed datasets, with an extensive focus on supervised learning (SL) problems …

Federated learning (FL) facilitates collaboration between a group of clients who seek to train
a common machine learning model without directly sharing their local data. Although there …

Abstract In Federated Learning (FL), multiple clients collaborate to learn a shared model
through a central server while keeping data decentralized. Personalized Federated …