FEDLAD：深度泄漏攻击与防御的联邦评估
FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses
AI总结：
本文提出 FEDLAD 统一基准，在现实联邦学习场景中评估多种深度泄漏攻击与防御方法，揭示隐私与模型准确率的权衡并提升可复现性。
摘要：
联邦学习是一种保护隐私的去中心化机器学习范式，旨在通过在多个客户端之间协同训练模型，将梯度交换给服务器，同时将私有数据保留在本地。然而，近期研究表明，联邦学习的安全性受到了损害，因为私有的真实数据可以通过一种被称为深度泄漏（Deep Leakage）的梯度反演技术被恢复。尽管这些攻击在设计时聚焦于联邦学习中的应用，但它们通常没有在现实场景中得到评估。本文介绍了 FEDLAD 框架（Federated Evaluation of Deep Leakage Attacks and Defenses，深度泄漏攻击与防御的联邦评估），这是一个用于在现实联邦环境中评估深度泄漏攻击与防御的综合基准。通过实现一个统一基准，其中包含多种最先进的深度泄漏技术和各种防御策略，我们的框架有助于在不同数据集和训练状态下评估并比较这些方法的有效性。本工作强调了联邦学习中隐私与模型准确率之间的关键权衡，旨在增进对去中心化机器学习系统中安全挑战的理解，激励未来研究，并提升深度泄漏攻击与防御评估的可复现性。
英文摘要：
Federated Learning is a privacy preserving decentralized machine learning paradigm designed to collaboratively train models across multiple clients by exchanging gradients to the server and keeping private data local. Nevertheless, recent research has revealed that the security of Federated Learning is compromised, as private ground truth data can be recovered through a gradient inversion technique known as Deep Leakage. While these attacks are crafted with a focus on applications in Federated Learning, they generally are not evaluated in realistic scenarios. This paper introduces the FEDLAD Framework (Federated Evaluation of Deep Leakage Attacks and Defenses), a comprehensive benchmark for evaluating Deep Leakage attacks and defenses within a realistic Federated context. By implementing a unified benchmark that encompasses multiple state-of-the-art Deep Leakage techniques and various defense strategies, our framework facilitates the evaluation and comparison of the efficacy of these methods across different datasets and training states. This work highlights a crucial trade-off between privacy and model accuracy in Federated Learning and aims to advance the understanding of security challenges in decentralized machine learning systems, stimulate future research, and enhance reproducibility in evaluating Deep Leakage attacks and defenses.
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。
