Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

The rapid advancement of artificial intelligence technologies has given rise to diversified
intelligent services, which place unprecedented demands on massive connectivity and …

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by
enhancing the trust of end-users in machines. As the number of connected devices keeps on …

Federated learning (FL) leverages the private data and computing power of multiple clients
to collaboratively train a global model. Many existing FL algorithms over wireless networks …

Federated learning (FL), as an emerging edge artificial intelligence paradigm, enables many
edge devices to collaboratively train a global model without sharing their private data. To …

Federated learning is a new paradigm to support resource-intensive and privacy-aware
learning applications. It enables the Internet-of-Things (IoT) devices to collaboratively train a …

As data generation increasingly takes place on devices without a wired connection, Machine
Learning (ML) related traffic will be ubiquitous in wireless networks. Many studies have …

In this paper, we consider communication-efficient over-the-air federated learning (FL),
where multiple edge devices with non-independent and identically distributed datasets …

This paper proposes a vehicular edge federated learning (VEFL) solution, where an edge
server leverages highly mobile connected vehicles'(CVs') onboard central processing units …

Federated learning (FL), as a disruptive machine learning (ML) paradigm, enables the
collaborative training of a global model over decentralized local data sets without sharing …