Communication and computation are often viewed as separate tasks. This approach is very
effective from the perspective of engineering as isolated optimizations can be performed …

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

The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …

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 …

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 …

The dramatic success of deep learning is largely due to the availability of data. Data
samples are often acquired on edge devices, such as smartphones, vehicles, and sensors …

Over-the-air computation (AirComp) based federated learning (FL) is capable of achieving
fast model aggregation by exploiting the waveform superposition property of multiple-access …

Federated Learning (FL) refers to distributed protocols that avoid direct raw data exchange
among the participating devices while training for a common learning task. This way, FL can …

In this paper, we study the problem of federated learning (FL) over a wireless channel,
modeled by a Gaussian multiple access channel (MAC), subject to local differential privacy …

Over-the-air federated edge learning (Air-FEEL) has emerged as a communication-efficient
solution to enable distributed machine learning over edge devices by using their data locally …