As the standardization of 5G solidifies, researchers are speculating what 6G will be. The
integration of sensing functionality is emerging as a key feature of the 6G Radio Access …

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

Driven by the interplay among artificial intelligence, digital twin, and wireless networks, 6G is
envisaged to go beyond data-centric services to provide intelligent and immersive …

Signal processing, communications, and control have traditionally relied on classical
statistical modeling techniques. Such model-based methods utilize mathematical …

We present a novel approach to federated learning that endows its aggregation process with
greater robustness to potential poisoning of local data or model parameters of participating …

We focus on over-the-air (OTA) Federated Learning (FL), which has been suggested
recently to reduce the communication overhead of FL due to the repeated transmissions of …

In this article, a general introduction to the area of sensor array and multichannel signal
processing is provided, including associated activities of the IEEE Signal Processing Society …

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines
on-device training and server-based aggregation to train a common ML model among …

Federated learning (FL) is an emerging paradigm for training machine learning models
using possibly private data available at edge devices. The distributed operation of FL gives …

This article integrates nonorthogonal multiple access (NOMA) and over-the-air federated
learning (AirFL) into a unified framework using one simultaneous transmitting and reflecting …