Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the
massive volume of data generated from ubiquitous mobile devices for enabling intelligent …

Federated learning (FL) is seen as a road toward privacy-preserving distributed artificial
intelligence while keeping raw training data on local devices. By leveraging blockchain, this …

Federated learning (FL) has experienced a boom in recent years, which is jointly promoted
by the prosperity of machine learning and Artificial Intelligence along with emerging privacy …

The arrival of the fifth-generation technology standard for broadband cellular networks (5G)
and beyond 5G networks (B5G) rises the speed and robustness ceiling of communicating …

Federated learning (FL) offers a promising approach to efficient machine learning with
privacy protection in distributed environments, such as Internet of Things (IoT) and mobile …

Federated learning (FL) is a distributed machine learning (ML) technique that enables
collaborative training in which devices perform learning using a local dataset while …

The role of the Internet of Things (IoT) in the revolutionized society cannot be overlooked.
The IoT can leverage advanced machine learning (ML) algorithms for its applications …

Motivated by the increasingly powerful computing capabilities of end-user equipment, and
by the growing privacy concerns over sharing sensitive raw data, a distributed machine …

Federated learning (FL) enables collaborative training of machine learning (ML) models
while preserving user data privacy. Existing FL approaches can potentially facilitate …

The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering
significant advantages in agility, responsiveness, and potential environmental benefits. The …