Federated Learning (FL) is an emerging paradigm that holds great promise for
privacy-preserving machine learning using distributed data. To enhance privacy,
FL can be combined with Differential Privacy (DP), which involves adding
Gaussian noise to the model weights. However, FL faces a significant challenge
in terms of large communication overhead when transmitting these model weights.
To address this issue, quantization is commonly employed. Nevertheless, the
presence of quantized Gaussian noise introduces complexities in understanding
privacy protection. This research paper investigates the impact of quantization
on privacy in FL systems. We examine the privacy guarantees of quantized
Gaussian mechanisms using R\'enyi Differential Privacy (RDP). By deriving the
privacy budget of quantized Gaussian mechanisms, we demonstrate that lower
quantization bit levels provide improved privacy protection. To validate our
theoretical findings, we employ Membership Inference Attacks (MIA), which gauge
the accuracy of privacy leakage. The numerical results align with our
theoretical analysis, confirming that quantization can indeed enhance privacy
protection. This study not only enhances our understanding of the correlation
between privacy and communication in FL but also underscores the advantages of
quantization in preserving privacy.

通过分析量化对联邦学习系统中的隐私保护的影响，本研究论文得出了量化位级越低，隐私保护越好的结论，并通过隶属推理攻击验证了这一理论分析，证实了量化确实可以增强隐私保护。

联邦学习中量化的效果：一种以 Rényi 差分隐私为视角的研究

The Effect of Quantization in Federated Learning: A Rényi Differential  Privacy Perspective

Smartwatch health sensor data is increasingly utilized in smart health
applications and patient monitoring, including stress detection. However, such
medical data often comprises sensitive personal information and is
resource-intensive to acquire for research purposes. In response to this
challenge, we introduce the privacy-aware synthetization of multi-sensor
smartwatch health readings related to moments of stress. Our method involves
the generation of synthetic sequence data through Generative Adversarial
Networks (GANs), coupled with the implementation of Differential Privacy (DP)
safeguards for protecting patient information during model training. To ensure
the integrity of our synthetic data, we employ a range of quality assessments
and monitor the plausibility between synthetic and original data. To test the
usefulness, we create private machine learning models on a commonly used,
albeit small, stress detection dataset, exploring strategies for enhancing the
existing data foundation with our synthetic data. Through our GAN-based
augmentation methods, we observe improvements in model performance, both in
non-private (0.45% F1) and private (11.90-15.48% F1) training scenarios. We
underline the potential of differentially private synthetic data in optimizing
utility-privacy trade-offs, especially with limited availability of real
training samples.

随着智能手表健康传感器数据在智能医疗应用和患者监测中的应用越来越广泛，其中包括压力检测，然而，这类医学数据往往包含敏感的个人信息且获取成本较高。为了应对这一挑战，我们介绍了隐私意识的合成多传感器智能手表健康读数和压力瞬间相关性的方法。我们的方法使用生成对抗网络（GANs）生成合成序列数据，并结合差分隐私（DP）技术在模型训练过程中保护患者信息。为了确保合成数据的可靠性，我们采用了一系列质量评估方法，并监测合成数据与原始数据之间的合理性。为了测试其实用性，我们在一个常用但规模较小的压力检测数据集上创建了私有的机器学习模型，并探索了如何通过合成数据来增强现有数据基础。通过我们基于 GAN 的增强方法，我们观察到模型在非隐私（0.45% F1）和隐私（11.90-15.48% F1）训练场景中的性能改善。我们强调了差分隐私合成数据在优化效用 - 隐私权衡中的潜力，尤其在真实训练样本有限的情况下。