In this article, we propose a novel federated learning (FL) framework for wireless Internet of
Medical Things (IoMT) based healthcare systems, where multiple mobile clients and one …

Federated Learning (FL) allows clients to form a consortium to train a global model under
the orchestration of a central server while keeping data on the local client without sharing it …

In recent years, deep learning methods based on a large amount of data have achieved
substantial success in numerous fields. However, with increases in regulations for protecting …

The growing interest in intelligent services and privacy protection for mobile devices has
given rise to the widespread application of federated learning in Multi-access Edge …

Prevalent federated learning commonly develops under the assumption that the ideal global
class distributions are balanced. In contrast, real-world data typically follows the long-tailed …

Federated learning has emerged as a promising approach for collaborative model training
across distributed devices. Federated learning faces challenges such as Non-Independent …

Federated Long-Tailed Learning (Fed-LT), a paradigm wherein data collected from
decentralized local clients manifests a globally prevalent long-tailed distribution, has …

Federated learning (FL) offers a privacy-centric distributed learning framework, enabling
model training on individual clients and central aggregation without necessitating data …

Federated Learning (FL) has become a popular distributed learning paradigm that involves
multiple clients training a global model collaboratively in a data privacy-preserving manner …

Federated learning (FL) is a distributed framework for collaborative training with privacy
guarantees. In real-world scenarios, clients may have Non-IID data (local class imbalance) …