Federated learning has been explored as a promising solution for training machine learning
models at the network edge, without sharing private user data. With limited resources at the …

Federated learning allows distributed clients to train a shared machine learning model while
preserving user privacy. In this framework, user devices (ie, clients) perform local iterations …

Federated learning is a newly emerged distributed deep learning paradigm, where the
clients separately train their local neural network models with private data and then jointly …

New technologies bring opportunities to deploy AI and machine learning to the edge of the
network, allowing edge devices to train simple models that can then be deployed in practice …

Federated learning (FL) is a transformative approach to Machine Learning that enables the
training of a shared model without transferring private data to a central location. This …

This work presents an efficient data-centric client selection approach, named DICE, to
enable federated learning (FL) over distributed edge networks. Prior research focused on …

Federated Learning is a promising technique for providing distributed learning without
clients disclosing their private data. In Hierarchical Federated Learning, edge servers …

Under the federated learning paradigm, a set of nodes can cooperatively train a machine
learning model with the help of a centralized server. Such a server is also tasked with …

Low latency, resource efficiency, and data privacy are some of the crucial requirements in
modern communication networks. Federated learning can efficiently address these issues …

Federated Learning (FL) is a state-of-the-art paradigm used in Edge Computing (EC). It
enables distributed learning to train on cross-device data, achieving efficient performance …