Federated learning emerges as a solution to the dilemma of data silos while safeguarding
data privacy, particularly relevant in the consumer electronics sector where user data privacy …

In this paper, the problem of energy efficient transmission and computation resource
allocation for federated learning (FL) over wireless communication networks is investigated …

In this article, the problem of training federated learning (FL) algorithms over a realistic
wireless network is studied. In the considered model, wireless users execute an FL …

Fueled by the availability of more data and computing power, recent breakthroughs in cloud-
based machine learning (ML) have transformed every aspect of our lives from face …

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …

Federated learning is a newly emerged distributed deep learning paradigm, where the
clients separately train their local neural network models with private data and then jointly …

Machine learning is one of the key building blocks in 5G and beyond [1–3], spanning a
broad range of applications and use cases. In the context of mission-critical applications [2 …

Distributed Artificial Intelligence (AI) model training over mobile edge networks encounters
significant challenges due to the data and resource heterogeneity of edge devices. The …

Federated Learning (FL), which enables multiple clients to cooperatively train global models
without revealing private data, has gained significant attention from researchers in recent …

This letter proposes a novel communication-efficient and privacy-preserving distributed
machine learning framework, coined Mix2FLD. To address uplink-downlink capacity …