DP$^2$-FedSAM：通过个性化锐度感知最小化增强差分隐私联邦学习
DP$^2$-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization
AI总结：
本文提出DP$^2$-FedSAM，结合个性化部分模型共享与锐度感知最小化，在差分隐私联邦学习中减轻噪声与裁剪影响，提升模型效用，尤其在数据异构场景下改善隐私-效用权衡。
摘要：
联邦学习（FL）是一种分布式机器学习方法，允许多个客户端在不共享原始数据的情况下协同训练模型。为了防止敏感信息通过FL中共享的模型更新被推断出来，研究者提出了差分隐私联邦学习（DPFL）。DPFL通过对共享的模型更新进行裁剪和添加随机噪声，在FL中提供正式且严格的隐私保护。然而，现有的DPFL方法常常导致严重的模型效用下降，尤其是在数据异构的设置中。为了提升模型效用，我们提出了一种新颖的DPFL方法，名为DP$^2$-FedSAM：具有锐度感知最小化的差分隐私与个性化联邦学习。DP$^2$-FedSAM利用个性化的部分模型共享和锐度感知最小化优化器来减轻噪声添加和裁剪的不利影响，从而在不牺牲隐私的前提下显著提升模型效用。从理论角度看，我们对我们所提出方法的隐私和收敛性保证提供了严格的理论分析。为了评估DP$^2$-FedSAM的有效性，我们基于常见的基准数据集进行了广泛的评估。我们的结果验证了，与现有的DPFL方法相比，我们的方法改善了隐私-效用权衡，特别是在异构数据设置中。
英文摘要：
Federated learning (FL) is a distributed machine learning approach that allows multiple clients to collaboratively train a model without sharing their raw data. To prevent sensitive information from being inferred through the model updates shared in FL, differentially private federated learning (DPFL) has been proposed. DPFL ensures formal and rigorous privacy protection in FL by clipping and adding random noise to the shared model updates. However, the existing DPFL methods often result in severe model utility degradation, especially in settings with data heterogeneity. To enhance model utility, we propose a novel DPFL method named DP$^2$-FedSAM: Differentially Private and Personalized Federated Learning with Sharpness-Aware Minimization. DP$^2$-FedSAM leverages personalized partial model-sharing and sharpness-aware minimization optimizer to mitigate the adverse impact of noise addition and clipping, thereby significantly improving model utility without sacrificing privacy. From a theoretical perspective, we provide a rigorous theoretical analysis of the privacy and convergence guarantees of our proposed method. To evaluate the effectiveness of DP$^2$-FedSAM, we conduct extensive evaluations based on common benchmark datasets. Our results verify that our method improves the privacy-utility trade-off compared to the existing DPFL methods, particularly in heterogeneous data settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
