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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2306.09912 |
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| _version_ | 1866909290632052736 |
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| author | Ren, Chao Yan, Rudai Zhu, Huihui Yu, Han Xu, Minrui Shen, Yuan Xu, Yan Xiao, Ming Dong, Zhao Yang Skoglund, Mikael Niyato, Dusit Kwek, Leong Chuan |
| author_facet | Ren, Chao Yan, Rudai Zhu, Huihui Yu, Han Xu, Minrui Shen, Yuan Xu, Yan Xiao, Ming Dong, Zhao Yang Skoglund, Mikael Niyato, Dusit Kwek, Leong Chuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_09912 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Towards Quantum Federated Learning Ren, Chao Yan, Rudai Zhu, Huihui Yu, Han Xu, Minrui Shen, Yuan Xu, Yan Xiao, Ming Dong, Zhao Yang Skoglund, Mikael Niyato, Dusit Kwek, Leong Chuan Machine Learning Quantum Physics 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. |
| title | Towards Quantum Federated Learning |
| topic | Machine Learning Quantum Physics |
| url | https://arxiv.org/abs/2306.09912 |