In a data-centric era, concerns regarding privacy and ethical data handling grow as machine learning relies more on personal information. This empirical study investigates the privacy, generalization, and stability of deep learning models in the presence of additive noise in federated learning frameworks. Our main objective is to provide strategies to measure the generalization, stability, and privacy-preserving capabilities of these models and further improve them. To this end, five noise infusion mechanisms at varying noise levels within centralized and federated learning settings are explored. As model complexity is a key component of the generalization and stability of deep learning models during training and evaluation, a comparative analysis of three Convolutional Neural Network (CNN) architectures is provided. The paper introduces Signal-to-Noise Ratio (SNR) as a quantitative measure of the trade-off between privacy and training accuracy of noise-infused models, aiming to find the noise level that yields optimal privacy and accuracy. Moreover, the Price of Stability and Price of Anarchy are defined in the context of privacy-preserving deep learning, contributing to the systematic investigation of the noise infusion strategies to enhance privacy without compromising performance. Our research sheds light on the delicate balance between these critical factors, fostering a deeper understanding of the implications of noise-based regularization in machine learning. By leveraging noise as a tool for regularization and privacy enhancement, we aim to contribute to the development of robust, privacy-aware algorithms, ensuring that AI-driven solutions prioritize both utility and privacy.

翻译：在数据驱动的时代，随着机器学习对个人信息的依赖日益加深，隐私与伦理数据处理的关切也随之增长。本实证研究探讨了联邦学习框架下添加噪声对深度学习模型隐私性、泛化性与稳定性的影响。我们的主要目标是提供衡量这些模型泛化性、稳定性与隐私保护能力的策略，并进一步加以改进。为此，我们研究了集中式与联邦学习场景中五种不同噪声水平下的噪声注入机制。鉴于模型复杂度是深度学习模型在训练与评估过程中泛化性与稳定性的关键因素，我们对三种卷积神经网络架构进行了比较分析。论文引入信噪比作为衡量噪声注入模型隐私性与训练准确率之间权衡的量化指标，旨在寻找能兼顾最优隐私性与准确性的噪声水平。此外，我们在隐私保护深度学习的语境中定义了稳定性代价与无政府代价，为系统研究在保障性能前提下通过噪声注入策略提升隐私性做出贡献。本研究揭示了这些关键因素之间的微妙平衡，促进了对机器学习中基于噪声的正则化方法更深入的理解。通过将噪声作为正则化与隐私增强的工具，我们旨在推动鲁棒、隐私感知算法的开发，确保人工智能解决方案既能兼顾效用又能保护隐私。