# Differentially Private Federated Quantum Learning via Quantum Noise

arXiv: https://arxiv.org/abs/2508.20310 · HTML: https://mlsanity.com/paper/2508.20310/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 27 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [The Composition Theorem for Differential Privacy](https://mlsanity.com/paper/1311.0776/) (2013)
- [Quantum noise protects quantum classifiers against adversaries](https://mlsanity.com/paper/2003.09416/) (2020)
- [Quantum Adversarial Machine Learning](https://mlsanity.com/paper/2001.00030/) (2019)
- [Quantum Differential Privacy: An Information Theory Perspective](https://mlsanity.com/paper/2202.10717/) (2022)
- [QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning](https://mlsanity.com/paper/2202.13239/) (2022)
- [Federated Quantum Machine Learning with Differential Privacy](https://mlsanity.com/paper/2310.06973/) (2023)
- [Bias and Variance of Post-processing in Differential Privacy](https://mlsanity.com/paper/2010.04327/) (2020)
- [IBM Quantum Computers: Evolution, Performance, and Future Directions](https://mlsanity.com/paper/2410.00916/) (2024)
- [Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach](https://mlsanity.com/paper/2411.10619/) (2024)
- [Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approach](https://mlsanity.com/paper/2507.08217/) (2025)
- [Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise](https://mlsanity.com/paper/2507.12492/) (2025)
- [Error Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysis](https://mlsanity.com/paper/2209.11514/) (2022)
- [Quantum Adversarial Learning for Kernel Methods](https://mlsanity.com/paper/2404.05824/) (2024)
- [Electrocardiogram (ECG) Based Cardiac Arrhythmia Detection and Classification using Machine Learning Algorithms](https://mlsanity.com/paper/2412.05583/) (2024)
- [Harnessing Inherent Noises for Privacy Preservation in Quantum Machine Learning](https://mlsanity.com/paper/2312.11126/) (2023)

## Built on by (most cited first, 2 of 2)

- [Quantum Federated Learning: Architectural Elements and Future Directions](https://mlsanity.com/paper/2510.17642/) (2025)
- [Quantum Federated Learning Based on Bures--Uhlmann Geometry for Heterogeneous Noisy Clients](https://mlsanity.com/paper/2608.28379/) (2026)

## Abstract (arXiv)

Quantum federated learning (QFL) enables collaborative training of quantum machine learning (QML) models across distributed quantum devices without raw data exchange. However, QFL remains vulnerable to adversarial attacks, where shared QML model updates can be exploited to undermine information privacy. In the context of noisy intermediate-scale quantum (NISQ) devices, a key question arises: How can inherent quantum noise be leveraged to enforce differential privacy (DP) and protect model information during training and communication? This paper explores a novel DP mechanism that harnesses quantum noise to safeguard quantum models throughout the QFL process. By tuning noise variance through measurement shots and depolarizing channel strength, our approach achieves desired DP levels tailored to NISQ constraints. Simulations demonstrate the framework's effectiveness by examining the relationship between differential privacy budget and noise parameters, as well as the trade-off between security and training accuracy. Additionally, we demonstrate the framework's robustness against an adversarial attack designed to compromise model performance using adversarial examples, with evaluations based on critical metrics such as accuracy on adversarial examples, confidence scores for correct predictions, and attack success rates. The results reveal a tunable trade-off between privacy and robustness, providing an efficient solution for secure QFL on NISQ devices with significant potential for reliable quantum computing applications.