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 …

Over-the-air federated edge learning (Air-FEEL) has emerged as a promising solution to
support edge artificial intelligence (AI) in future, beyond 5G (B5G) and 6G networks. In Air …

Gradient inversion attacks (GIAs) have posed significant challenges to the emerging
paradigm of distributed learning, which aims to reconstruct the private training data of clients …

In this paper, the application of federated learning to smart farming has been investigated.
The Federated averaging model has been used to carry out crop classification using climatic …

Federated edge learning (FL), as an emerging distributed machine learning paradigm,
allows a mass of edge devices to collaboratively train a global model while preserving …

Federated learning (FL) is vulnerable to backdoor attacks due to its distributed computing
nature. Existing defense solution usually requires larger amount of computation in either the …

Cell-free massive MIMO is emerging as a promising technology for future wireless
communication systems, which is expected to offer uniform coverage and high spectral …

Crop improvement and production domains encounter large amounts of expanding data
with multi-layer complexity that forces researchers to use machine-learning approaches to …

Over-the-air computation (AirComp) integrates analog communication with task-oriented
computation, serving as a key enabling technique for communication-efficient federated …

The data heterogeneity across clients and the limited communication resources, eg,
bandwidth and energy, are two of the main bottlenecks for wireless federated learning (FL) …