[2306.09912] Towards Quantum Federated Learning
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Computer Science > Machine Learning

arXiv:2306.09912 (cs)
[Submitted on 16 Jun 2023 (v1), last revised 19 Aug 2024 (this version, v4)]

Title:Towards Quantum Federated Learning

Authors:Chao Ren, Rudai Yan, Huihui Zhu, Han Yu, Minrui Xu, Yuan Shen, Yan Xu, Ming Xiao, Zhao Yang Dong, Mikael Skoglund, Dusit Niyato, Leong Chuan Kwek
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Abstract:Quantum Federated Learning (QFL) is an emerging interdisciplinary field that merges the principles of Quantum Computing (QC) and Federated Learning (FL), with the goal of leveraging quantum technologies to enhance privacy, security, and efficiency in the learning process. Currently, there is no comprehensive survey for this interdisciplinary field. This review offers a thorough, holistic examination of QFL. We aim to provide a comprehensive understanding of the principles, techniques, and emerging applications of QFL. We discuss the current state of research in this rapidly evolving field, identify challenges and opportunities associated with integrating these technologies, and outline future directions and open research questions. We propose a unique taxonomy of QFL techniques, categorized according to their characteristics and the quantum techniques employed. As the field of QFL continues to progress, we can anticipate further breakthroughs and applications across various industries, driving innovation and addressing challenges related to data privacy, security, and resource optimization. This review serves as a first-of-its-kind comprehensive guide for researchers and practitioners interested in understanding and advancing the field of QFL.
Comments: Survey of quantum federated learning (QFL)
Subjects: Machine Learning (cs.LG); Quantum Physics (quant-ph)
Cite as: arXiv:2306.09912 [cs.LG]
  (or arXiv:2306.09912v4 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2306.09912

Submission history

From: Chao Ren [view email]
[v1] Fri, 16 Jun 2023 15:40:21 UTC (976 KB)
[v2] Fri, 2 Feb 2024 03:27:33 UTC (1,204 KB)
[v3] Mon, 5 Feb 2024 05:04:07 UTC (1,205 KB)
[v4] Mon, 19 Aug 2024 17:58:55 UTC (2,247 KB)
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