Conventional machine learning approaches aggregate all training data in a central server,
which causes massive communication overhead of data transmission and is also vulnerable …

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 fast development of mobile communication and artificial intelligence (AI) technologies
greatly promotes the prosperity of the Internet of Things (IoT), where various types of IoT …

Federated learning (FL) has been proposed as an emerging paradigm to perform privacy-
preserving distributed machine learning in the Internet of Things (IoT). However, the …

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 …

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) serves as an enabling technology for intelligent edge computing,
where high-quality machine learning (ML) models are collaboratively trained over large …

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 …

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

The fast proliferation of edge computing devices brings an increasing growth of data, which
directly promotes machine learning (ML) technology development. However, privacy issues …