Foundations of Quantum Federated Learning Over Classical and Quantum Networks

Quantum federated learning (QFL) is a novel framework that integrates the advantages of classical federated learning (FL) with the computational power of quantum technologies. This includes quantum computing and quantum machine learning (QML), enabling QFL to handle high-dimensional complex data. QF...

Full description

Saved in:
Bibliographic Details
Published inIEEE network Vol. 38; no. 1; pp. 124 - 130
Main Authors Chehimi, Mahdi, Chen, Samuel Yen-Chi, Saad, Walid, Towsley, Don, Debbah, Merouane
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Communication networks
Federated learning
Hardware
Integrated circuit modeling
Logic gates
Machine learning
Quantum computing
Qubit
Servers
Training
Online AccessGet full text

Cover

More Information
Summary:Quantum federated learning (QFL) is a novel framework that integrates the advantages of classical federated learning (FL) with the computational power of quantum technologies. This includes quantum computing and quantum machine learning (QML), enabling QFL to handle high-dimensional complex data. QFL can be deployed over both classical and quantum communication networks in order to benefit from informationtheoretic security levels surpassing traditional FL frameworks. In this paper, we provide the first comprehensive investigation of the challenges and opportunities of QFL. We particularly examine the key components of QFL and identify the unique challenges that arise when deploying it over both classical and quantum networks. We then develop novel solutions and articulate promising research directions that can help address the identified challenges. We also provide actionable recommendations to advance the practical realization of QFL.
ISSN:0890-8044
1558-156X
DOI:10.1109/MNET.2023.3327365