The advent of federated learning (FL) has sparked a new paradigm of parallel and
confidential decentralized machine learning (ML) with the potential of utilizing the …

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), as a distributed machine learning technology, allows large-scale
nodes to utilize local datasets for model training and sharing without revealing privacy …

Digital twin (DT), referring to a promising technique to digitally and accurately represent
actual physical entities, has attracted explosive interests from both academia and industry …

Federated learning (FL) facilitates collaboration between a group of clients who seek to train
a common machine learning model without directly sharing their local data. Although there …

Federated learning (FL) is a new and promising paradigm that allows devices to learn
without sharing data with the centralized server. It is often built on decentralized data where …

Limited access to large-scale data is a key obstacle to building machine learning (ML)
applications in practice, partly due to a reluctance of information exchange among data …

In federated learning (FL), due to the non-iid nature of distributedly owned local datasets,
personalization is an important design goal. In this paper, we investigate FL scenarios in …

This paper presents a game theoretic framework for participation and free-riding in federated
learning (FL), and determines the Nash equilibrium strategies when FL is executed over …

Federated learning (FL) is a machine learning approach that decentralizes data and its
processing by allowing clients to train intermediate models on their devices with locally …