The deployment of federated learning in a wireless network, called federated edge learning
(FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model …

Federated learning (FL) is a powerful distributed machine learning framework where a
server aggregates models trained by different clients without accessing their private data …

Cooperative training methods for distributed machine learning typically assume noiseless
and ideal communication channels. This work studies some of the opportunities and …

Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …

The rapidly expanding number of Internet of Things (IoT) devices is generating huge
quantities of data, but public concern over data privacy means users are apprehensive to …

This paper aims to develop distributed learning algorithms for Random Vector Functional-
Link (RVFL) networks, where training data is distributed under a decentralized information …

To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …

Emerging technologies and applications including Internet of Things, social networking, and
crowd-sourcing generate large amounts of data at the network edge. Machine learning …

Federated learning (FL) provides a promising framework for enabling distributed machine
learning based services without revealing users' private data. In the scenario of wireless FL …

Deep learning (DL), despite its enormous success in many computer vision and language
processing applications, is exceedingly vulnerable to adversarial attacks. We consider the …