The ever-growing artificial intelligence (AI) applications have greatly reshaped our world in
many areas, eg, smart home, computer vision, natural language processing, etc. Behind …

In this paper, we present a three-layer (ie, device, field, and factory layers) deterministic
federated learning (FL) framework, named DetFed, which accelerates collaborative learning …

The deep fusion of human-centered Cyber-Physical-Social Systems (CPSSs) has attracted
widespread attention worldwide and big data as the blood of CPSSs could lay a solid data …

In this paper, we study a relay-assisted federated edge learning (FEEL) network under
latency and bandwidth constraints. In this network, users collaboratively train a global model …

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 …

Distributed machine learning (ML) algorithms are imperative for the next-generation Internet
of Things (IoT) networks, thanks to preserving the privacy of users' data and efficient usage …

Federated learning (FL) has emerged as a distributed machine learning (ML) technique to
train models without sharing users' private data. In this paper, we introduce a decentralized …

Federated learning (FL) has recently emerged as a promising technology to enable artificial
intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a …

Introducing adaptiveness to federated learning has recently ushered in a new way to
optimize its convergence performance. However, adaptive learning strategies originally …

Federated learning (FL) can suffer from communication bottlenecks when deployed in
mobile networks, limiting participating clients and deterring FL convergence. In this context …