New Insights on Unfolding and Fine-tuning Quantum Federated Learning
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Abstract (arXiv)
Client heterogeneity poses significant challenges to the performance of Quantum Federated Learning (QFL). To overcome these limitations, we propose a new approach leveraging deep unfolding, which enables clients to autonomously optimize hyperparameters, such as learning rates and regularization factors, based on their specific training behavior. This dynamic adaptation mitigates overfitting and ensures robust optimization in highly heterogeneous environments where standard aggregation methods often fail. Our framework achieves approximately 90% accuracy, significantly outperforming traditional methods, which typically yield around 55% accuracy, as demonstrated through real-time training on IBM quantum hardware and Qiskit Aer simulators. By developing self adaptive fine tuning, the proposed method proves particularly effective in critical applications such as gene expression analysis and cancer detection, enhancing diagnostic precision and predictive modeling within quantum systems. Our results are attributed to convergence-aware, learnable optimization steps intrinsic to the deep unfolded framework, which maintains the generalization. Hence, this study addresses the core limitations of conventional QFL, streamlining its applicability to any complex challenges such as healthcare and genomic research.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 45 references that are in the map. First posted on arXiv 24 Jun 2025.
Builds on
24 of 45 references.
References · 24
- 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
- 2018Supervised learning with quantum enhanced feature spacescited by 665
- 2019Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processingcited by 421
- 2021Federated Quantum Machine Learningcited by 82
- 2020Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognitioncited by 57
- 2021Quantum Federated Learning with Quantum Datacited by 53
- 2021Quantum federated learning through blind quantum computingcited by 44
- 2023Shadows of quantum machine learningcited by 31
- 2022Slimmable Quantum Federated Learningcited by 23
- 2021QuantumFed: A Federated Learning Framework for Collaborative Quantum Trainingcited by 22
- 2022Federated Learning with Quantum Secure Aggregationcited by 21
- 2023Foundations of Quantum Federated Learning Over Classical and Quantum Networkscited by 21
12 more
- 2023Towards Quantum Federated Learningcited by 20
- 2022Non-IID Quantum Federated Learning with One-shot Communication Complexitycited by 15
- 2023Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descentcited by 14
- 2022Federated Learning on Riemannian Manifoldscited by 13
- 2023Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernelscited by 8
- 2024Quantum Federated Learning Experiments in the Cloud with Data Encodingcited by 7
- 2023Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementationcited by 5
- 2022Towards Fairness-Aware Multi-Objective Optimizationcited by 4
- 2022Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Codingcited by 4
- 2024Riemannian Federated Learning via Averaging Gradient Streamscited by 4
- 2023Model-Driven Quantum Federated Learning (QFL)cited by 3
- 2025Stability and Generalization of Quantum Neural Networkscited by 3
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