Decentralized federated learning, inherited from decentralized learning, enables the edge
devices to collaborate on model training in a peer-to-peer manner without the assistance of …

Federated Learning (FL), an emerging paradigm for fast intelligent acquisition at the network
edge, enables joint training of a machine learning model over distributed data sets and …

Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized
federated learning (DFL). The performance of decentralized SGD is jointly influenced by …

Federated learning has emerged as a popular technique for distributing machine learning
(ML) model training across the wireless edge. In this paper, we propose two timescale …

Data generated at the network edge can be processed locally by leveraging the paradigm of
edge computing (EC). Aided by EC, decentralized federated learning (DFL), which …

In this paper, we consider decentralized federated learning (FL) over wireless networks,
where over-the-air computation (AirComp) is adopted to facilitate the local model consensus …

The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over
siloed data centers is motivating renewed interest in the collaborative training of a shared …

In the last few years, distributed machine learning has been usually executed over
heterogeneous networks such as a local area network within a multi-tenant cluster or a wide …

Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

Federated learning (FL) has emerged as a distributed machine learning (ML) technique to
train models without sharing users' private data. In this paper, we introduce a decentralized …