This paper analyzes the impact of imperfect communication channels on decentralized
federated learning (D-FL) and subsequently determines the optimal number of local …

Implementing Decentralized Gradient Descent (DGD) in wireless systems is challenging due
to noise, fading, and limited bandwidth, necessitating topology awareness, transmission …

Recent developments and emerging use cases, such as smart Internet of Things (IoT) and
Edge AI, have sparked considerable interest in the training of neural networks over fully …

In this work, we focus on the communication aspect of decentralized learning, which
involves multiple agents training a shared machine learning model using decentralized …

Robust machine learning (ML) models can be developed by leveraging large volumes of
data and distributing the computational tasks across numerous devices or servers …

Robust machine learning (ML) models can be developed by leveraging large volumes of
data and distributing the computational tasks across numerous devices or servers …

Decentralized learning (DL) is an emerging technique that allows nodes on the web to
collaboratively train machine learning models without sharing raw data. Dealing with …

In this work, we investigate the problem of an online trajectory design for an Unmanned
Aerial Vehicle (UAV) in a Federated Learning (FL) setting where several communities exist …

As a popular distributed machine learning framework, wireless federated edge learning
(FEEL) can keep original data local, while uploading model training updates to protect …

Decentralized learning empowers wireless network devices to collaboratively train a
machine learning (ML) model relying solely on device-to-device (D2D) communication. It is …