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 …

Federated Learning is a much-needed technology in this golden era of big data and Artificial
Intelligence, due to its vital role in preserving data privacy, and eliminating the need to …

In the traditional distributed machine learning scenario, the user's private data is transmitted
between clients and a central server, which results in significant potential privacy risks. In …

In this study, we propose an over-the-air computation (OAC) scheme to calculate the
majority vote (MV) for federated edge learning (FEEL). With the proposed approach, edge …

We focus on a distributed learning problem in a communication network, consisting of
distributed nodes and a central parameter server (PS). The PS is responsible for performing …

Channel fading can have a strong impact on the convergence of over-the-air federated edge
learning (OTA-FEEL). This paper develops a new and optimal power control policy to …

In this study, we propose a digital over-the-air computation (OAC) scheme for achieving
continuous-valued (analog) aggregation for federated edge learning (FEEL). We show that …

How to tackle the unreliability in wireless channels is critical for federated learning (FL). To
solve this problem, we propose a novel FL framework, namely FL with gradient recycling (FL …

This paper proposes and experimentally validates a new paradigm for computing with
wireless signals over-the-air (OTA). It demonstrates the first fully connected (FC) neural …

Deep Neural Network (DNN) splitting is one of the key enablers of edge Artificial Intelligence
(AI), as it allows end users to pre-process data and offload part of the computational burden …