Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial
participation) when the number of participants is large and the server's communication …

In this paper, we investigate how to deploy computational intelligence and deep learning
(DL) in edge-enabled industrial IoT networks. In this system, the IoT devices can …

The network edge is becoming a new solution for reducing latency and saving bandwidth in
the Internet of Things (IoT) network. The goal of the network edge is to move computation …

As a promising distributed learning paradigm, federated learning (FL) involves training deep
neural network (DNN) models at the network edge while protecting the privacy of the edge …

Federated learning enables learning from decentralized data sources without compromising
privacy, which makes it a crucial technique. However, it is vulnerable to model poisoning …

Industrial Internet of Things (IIoT) applications have diverse network session requirements.
Certain critical applications, such as emergency alert relays, as well as industrial floor …

Federated Learning (FL) is a promising research paradigm that enables the collaborative
training of machine learning models among various parties without the need for sensitive …

Existing machine learning (ML) model marketplaces generally require data owners to share
their raw data, leading to serious privacy concerns. Federated learning (FL) can partially …

The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-
efficient adaptive federated learning at the wireless network edge, with latency and learning …