联邦学习的安全性问题分析

Analysis on the Security of Federated Learning

摘要: 随着大数据和人工智能的快速发展，人们对数据安全的需求也日益提高。为了解决目前面临的隐私保护和数据孤岛问题，联邦学习受到了各界学者的广泛关注与研究。虽然联邦学习是一种很有潜力的机器学习技术，使位于不同地理位置的多个用户可以在不共享数据的情况下协作训练机器学习模型，但是也存在一些安全性问题。因此本文对联邦学习的安全性问题进行了总结和分析，这对联邦学习的发展及应用具有重要的意义。本文首先对联邦学习的基本概念和分类进行了详细地阐述；接着，深入分析了联邦学习面临的安全性问题，包括投毒攻击、后门攻击和基于生成对抗网络(GAN)的攻击；然后，归纳总结了不同攻击的防御方法。最后，对联邦学习的应用前景以及未来的研究方向进行了总结与展望。

Abstract: With the rapid development of big data and artificial intelligence, people’s demand for data security is also increasing. In order to solve the problems of privacy protection and data islands, federated learning has been widely concerned and studied by scholars from all walks of life. Although federated learning is a promising machine learning technology that enables multiple users in different geographical locations to collaborate on training machine learning models without sharing data, there are also some security issues. Therefore, this paper summarizes and analyzes the security issues of federated learning, which is of great significance to the development and application of federated learning. Firstly, this paper expounds the basic concepts and classification of federated learning in detail. Then, the security problems faced by federated learning are analyzed in depth, including poisoning attack, backdoor attack and Generative Adversarial Network (GAN)-based attack. Then, the defense methods of different attacks are summarized. Finally, the application prospect of federated learning and the future research direction are summarized and prospected.