系统梳理联邦学习的隐私安全挑战与防护策略
Trustworthy Federated Learning: Privacy, Security, and Beyond
- 综述联邦学习中通信链路与网络威胁的防御方法
- 指出分布式训练环境下隐私泄露与攻击风险
- 适合关注隐私保护与可信AI的研究者参考
尽管近年来大数据与人工智能取得显著进展，保障数据隐私与安全至关重要。联邦学习（FL）作为一种创新方法，通过在分布式数据源间协作训练模型而不传输原始数据，缓解了这一问题。然而，去中心化网络中的鲁棒性安全与隐私挑战引发广泛关注。本文对联邦学习中的安全与隐私问题进行了全面调研，揭示通信链路的脆弱性及潜在网络攻击风险。深入探讨多种防御策略，分析联邦学习在不同领域的应用，并提出未来研究方向。识别出联邦学习框架内复杂的安全挑战，旨在推动安全高效联邦学习系统的构建。
原文摘要 · Abstract (English)
While recent years have witnessed the advancement in big data and Artificial Intelligence (AI), it is of much importance to safeguard data privacy and security. As an innovative approach, Federated Learning (FL) addresses these concerns by facilitating collaborative model training across distributed data sources without transferring raw data. However, the challenges of robust security and privacy across decentralized networks catch significant attention in dealing with the distributed data in FL. In this paper, we conduct an extensive survey of the security and privacy issues prevalent in FL, underscoring the vulnerability of communication links and the potential for cyber threats. We delve into various defensive strategies to mitigate these risks, explore the applications of FL across different sectors, and propose research directions. We identify the intricate security challenges that arise within the FL frameworks, aiming to contribute to the development of secure and efficient FL systems.
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