Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …

In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

There has been an increasing interest in translating artificial intelligence (AI) research into
clinically-validated applications to improve the performance, capacity, and efficacy of …

Recent developments in the Internet of Things (IoT) and various communication
technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into …

Although remarkable progress has been made by existing federated learning (FL) platforms
to provide infrastructures for development, these platforms may not well tackle the …

The rise in popularity of Internet of Things (IoT) devices has attracted hackers to develop IoT-
specific attacks. The microservice architecture of IoT devices relies on the Internet to provide …

This article proposes a blockchain-based federated learning (FL) framework with Intel
Software Guard Extension (SGX)-based trusted execution environment (TEE) to securely …

The incredible development of federated learning (FL) has benefited various tasks in the
domains of computer vision and natural language processing, and the existing frameworks …

New technologies bring opportunities to deploy AI and machine learning to the edge of the
network, allowing edge devices to train simple models that can then be deployed in practice …

Graph machine learning has gained great attention in both academia and industry recently.
Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are …