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A Drift Stable Quantum Federated Learning for Intelligent Services

arXiv 2607.21647 ↗First posted 22 Jul 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

Quantum federated learning enables distributed clients to train quantum neural networks without sharing local data, making it promising for privacy-aware intelligent services. Intelligent services in this context refer to privacy-sensitive distributed decision systems, such as fraud detection and genomic classification, where reliable and fair client-level learning is as important as the accuracy of the aggregate model. However, heterogeneous client data and noisy quantum optimization often cause unstable local updates, client drift, and unfair performance between clients. This paper proposes DUQFL-Prox, a drift-stable quantum federated learning framework based on deep-unfolded local optimization. Instead of using a fixed local optimizer, each client performs adaptive unfolded SPSA updates, while a proximal term keeps the local model close to the global model. A lightweight controller learns step-specific optimization parameters to improve post-aggregation performance. Experiments on financial fraud and genomic classification tasks show that DUQFL-Prox improves stability, generalization, and client fairness compared with standard QFL baselines. The results suggest that deep-unfolded quantum federated learning can support more reliable and fair intelligent services in heterogeneous distributed environments.

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References · 19

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  10. 2024QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detectioncited by 18
  11. 2022Non-IID Quantum Federated Learning with One-shot Communication Complexitycited by 15
  12. 2023Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descentcited by 14
7 more
  1. 2024Quantum Federated Learning Experiments in the Cloud with Data Encodingcited by 7
  2. 2025Quantum Federated Learning: A Comprehensive Surveycited by 7
  3. 2025Deep Unfolding: Recent Developments, Theory, and Design Guidelinescited by 7
  4. 2023Mitigating Group Bias in Federated Learning for Heterogeneous Devicescited by 5
  5. 2022Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Codingcited by 4
  6. 2021A Deep Reinforcement Learning Approach for Composing Moving IoT Servicescited by 2
  7. 2025New Insights on Unfolding and Fine-tuning Quantum Federated Learningcited by 1

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