Blockchain, as the basis for cryptocurrencies, has received extensive attentions recently.
Blockchain serves as an immutable distributed ledger technology which allows transactions …

The conventional machine learning process typically operates under the premise of
centralized data aggregation, where all data is collected at a central location for model …

Edge intelligence, an emerging distributed paradigm, is driven by the increasing number of
Internet of Things devices and the development of edge computing and artificial intelligence …

Motivated by the heterogeneous nature of devices participating in large-scale federated
learning (FL) optimization, we focus on an asynchronous server-less FL solution …

Federated Learning (FL) is a distributed machine learning method that ensures the privacy
and security of participants' data by avoiding direct data upload to a central node for training …

Accurate prediction of vehicle trajectories on the Internet of Vehicles is crucial for improving
traffic safety. Federated learning has been proposed to address the issue of islands in …

In recent years, decentralized computing has gained popularity in various domains such as
decentralized learning, financial services and the Industrial Internet of Things. As identity …

Gaining significant attention within decentralized contexts, Federated Learning (FL) has
been positioned as a highly desirable method for machine learning. By enabling multiple …

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

In the face of issues such as privacy leakage and malicious attacks, blockchain-based
asynchronous federated learning emerges as a promising solution, not only capable of …