A Drift Stable Quantum Federated Learning for Intelligent Services

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 caus

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ethics.ai (22 July 2026), “A Drift Stable Quantum Federated Learning for Intelligent Services,” evidence record 13731, https://ethics.ai/record/13731 (originally published by arXiv fairness query).

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