In federated learning (FL), model training is distributed over clients and local models are
aggregated by a central server. The performance of uploaded models in such situations can …

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 …

The newly emerging federated learning (FL) framework offers a new way to train machine
learning models in a privacy-preserving manner. However, traditional FL algorithms are …

Federated learning (FL), arising as a privacy-preserving machine learning paradigm, has
received notable attention from the public. In each round of synchronous FL training, only a …

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 …

Federated learning (FL), as a type of collaborative machine learning framework, is capable
of preserving private data from mobile terminals (MTs) while training the data into useful …

Federated learning (FL) is a useful tool in distributed machine learning that utilizes users'
local datasets in a privacy-preserving manner. When deploying FL in a constrained wireless …

Federated learning (FL) leverages the private data and computing power of multiple clients
to collaboratively train a global model. Many existing FL algorithms over wireless networks …

As an emerging machine learning technique, federated learning has received significant
attention recently due to its promising performance in mitigating privacy risks and costs …

Federated learning (FL), as a type of distributed machine learning, is capable of significantly
preserving clients' private data from being exposed to adversaries. Nevertheless, private …