mlsanityData to 7 Oct 2026

Quantum Federated Learning: Analysis, Design and Implementation Challenges

arXiv 2306.15708 ↗First posted 27 Jun 2023quant-ph cs.LGcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 8 of its 15 references that are in the map. First posted on arXiv 27 Jun 2023.

Builds on

8 of 15 references.

References · 8

  1. 2020Variational Quantum Algorithmscited by 687
  2. 2021Federated Quantum Machine Learningcited by 82
  3. 2021Quantum Federated Learning with Quantum Datacited by 53
  4. 2021Quantum federated learning through blind quantum computingcited by 44
  5. 2022Slimmable Quantum Federated Learningcited by 23
  6. 2021QuantumFed: A Federated Learning Framework for Collaborative Quantum Trainingcited by 22
  7. 2022Federated Learning with Quantum Secure Aggregationcited by 21
  8. 2023Towards Quantum Federated Learningcited by 20

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 1.

6 papers.

  1. 2024MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryptioncited by 7
  2. 2025When Federated Learning Meets Quantum Computing: Survey and Research Opportunitiescited by 5
  3. 2025PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Frameworkcited by 4
  4. 2025Adversarial Robustness in Distributed Quantum Machine Learningcited by 2
  5. 2025Emerging Paradigms for Securing Federated Learning Systemscited by 0
  6. 2026Can Quantum Federated Learning Withstand Circuit-Level Backdoors?cited by 0