Quantum Federated Learning: Analysis, Design and Implementation Challenges
Abstract: Quantum Federated Learning (QFL) has gained significant attention due to quantum computing and machine learning advancements. As the demand for QFL continues to surge, there is a pressing need to comprehend its intricacies in distributed environments. This paper aims to provide a comprehensive overview of the current state of QFL, addressing a crucial knowledge gap in the existing literature. We develop ideas for new QFL frameworks, explore diverse use cases of applications, and consider the critical factors influencing their design. The technical contributions and limitations of various QFL research projects are examined while presenting future research directions and open questions for further exploration.
- Y. Zhang, C. Zhang, C. Zhang, L. Fan, B. Zeng, and Q. Yang, “Federated Learning with Quantum Secure Aggregation.” [Online]. Available: http://arxiv.org/abs/2207.07444
- S. Y.-C. Chen and S. Yoo, “Federated Quantum Machine Learning,” vol. 23, no. 4, p. 460.
- S. R. Pokhrel and J. Choi, “Federated Learning With Blockchain for Autonomous Vehicles: Analysis and Design Challenges,” vol. 68, no. 8, pp. 4734–4746.
- X. Pan et al., “Experimental quantum end-to-end learning on a superconducting processor,” vol. 9, no. 1, pp. 1–6. [Online]. Available: https://www.nature.com/articles/s41534-023-00685-w
- H. Zhao, “Exact Decomposition of Quantum Channels for Non-IID Quantum Federated Learning,” comment: 6 pages excluding appendices and references. Code available at https://github.com/JasonZHM/quantum-fed-infer. [Online]. Available: http://arxiv.org/abs/2209.00768
- Q. Xia and Q. Li, “QuantumFed: A Federated Learning Framework for Collaborative Quantum Training,” in 2021 IEEE Global Communications Conference (GLOBECOM), pp. 1–6.
- M. Chehimi and W. Saad, “Quantum Federated Learning with Quantum Data,” in ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8617–8621.
- W. Li, S. Lu, and D.-L. Deng, “Quantum federated learning through blind quantum computing,” vol. 64, no. 10, p. 100312. [Online]. Available: https://link.springer.com/10.1007/s11433-021-1753-3
- Q. Xia, Z. Tao, and Q. Li, “Defending Against Byzantine Attacks in Quantum Federated Learning,” in 2021 17th International Conference on Mobility, Sensing and Networking (MSN), pp. 145–152.
- W. Yamany, N. Moustafa, and B. Turnbull, “OQFL: An Optimized Quantum-Based Federated Learning Framework for Defending Against Adversarial Attacks in Intelligent Transportation Systems,” pp. 1–11.
- D. Gurung, S. R. Pokhrel, and G. Li, “Secure Communication Model for Quantum Federated learning: A Proof of Concept,” in ICLR 2023.
- C. Ren et al. Towards Quantum Federated Learning. arXiv.org. [Online]. Available: https://arxiv.org/abs/2306.09912v1
- R. Huang, X. Tan, and Q. Xu, “Quantum Federated Learning With Decentralized Data,” vol. 28, pp. 1–10.
- W. J. Yun et al., “Slimmable Quantum Federated Learning.”
- M. Cerezo and Others, “Variational Quantum Algorithms,” vol. 3, no. 9, pp. 625–644, comment: Review Article. 33 pages, 7 figures. Updated to published version. [Online]. Available: http://arxiv.org/abs/2012.09265
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