By making full use of the accumulative feature of wireless signals, the over-the-air
computation makes it possible for the federated learning to aggregate all the parameters …

Federated learning (FL) is recognized as a key enabling technology to provide intelligent
services for future wireless networks and industrial systems with delay and privacy …

Federated learning (FL) provides a promising framework for enabling distributed machine
learning based services without revealing users' private data. In the scenario of wireless FL …

This paper investigates the robustness of over-the-air federated learning to Byzantine
attacks. The simple averaging of the model updates via over-the-air computation makes the …

Federated learning (FL) is a type of distributed machine learning at the wireless edge that
preserves the privacy of clients' data from adversaries and even the central server. Existing …

Over-the-Air Computation is a beyond-5G communication strategy that has recently been
shown to be useful for the decentralized training of machine learning models due to its …

Federated Learning enables mobile devices to collaboratively learn a shared inference
model while keeping all the training data on a user's device, decoupling the ability to do …

In this paper, a novel secure and private over-the-air federated learning (SP-OTA-FL)
framework is studied where noise is employed to protect data privacy and system security …

Federated learning (FL) can protect clients' privacy from leakage in distributed machine
learning. Applying federated learning to edge computing can protect the privacy of edge …

The federated learning (FL) paradigm aims to distribute the computational burden of the
training process among several computation units, usually called agents or workers, while …