Federated learning (FL) and split learning (SL) are two emerging collaborative learning
methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT) …

Abstract Federated Learning (FL) enhances Artificial Intelligence (AI) applications by
enabling individual devices to collaboratively learn shared models without uploading local …

Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing
(VEC) to a certain extent through sharing the gradients of vehicles' local models instead of …

To fulfill people's expectations for smart and user-friendly Internet of Things (IoT)
applications, the quantity of processing is fast expanding, and task latency constraints are …

Abstract Distributed training of Machine Learning models in edge Internet of Things (IoT)
environments is challenging because of three main points. First, resource-constrained …

Federated learning (FL) is a machine learning technique that allows for on-site data
collection and processing without sacrificing data privacy and transmission. Heterogeneity is …

The internet of things (IoT) represents a disruptive concept that has been changing society in
several ways. There have been several successful applications of IoT in the industry. For …

As data privacy becomes increasingly important, federated learning applied to the training of
deep learning models while ensuring the data privacy of devices is entering the spotlight …

Federated Learning (FL) has emerged as a pivotal approach for decentralized Machine
Learning (ML), addressing the unique demands of the Internet of Things (IoT) environments …

The advent of the Artificial Intelligent Internet of Things (AIoT) has sparked a revolution in the
deployment of intelligent systems, driving the need for innovative data processing …