Federated learning (FL) is a kind of distributed machine learning framework, where the
global model is generated on the centralized aggregation server based on the parameters of …

Since the traffic conditions change over time, machine learning models that predict traffic
flows must be updated continuously and efficiently in smart public transportation. Federated …

With the widespread of 5G networks, the application of Federated Learning (FL) in Internet of
Things (IoT) has become a trend. However, the trust problem caused by the centralized …

Massive volumes of malware spread incidents continue to occur frequently across the
Internet of Things (IoT). Owing to its self-learning and adaptive capability, artificial …

Federated learning (FL) has shown promise in smart industries as a means of training
machine‐learning models while preserving privacy. However, it contradicts FL's low …

Video instance segmentation, also known as multi-object tracking and segmentation, is an
emerging computer vision research area introduced in 2019, aiming at detecting …

Opportunistic Federated Learning (OppCL) is a widely adopted distributed learning
approach for Intelligent Transportation Systems (ITS). However, the exchange of information …

Federated learning (FL) is a privacy-preserving approach in Artificial Intelligence (AI) that
involves exchanging intermediate training parameters instead of raw data, thereby avoiding …

This study addresses the critical challenge of crop diseases in global agriculture by
introducing a novel approach that combines wheat production, crop disease monitoring, and …

This study investigated three essential aspects of blockchain-enabled smart edge
computing: security, intelligence, and privacy. It strengthens the security of machine-to …