Covert communication has become an important area of research in computer security. It involves hiding specific information on a carrier for message transmission and is often used to transmit private data, military secrets, and even malware. In deep learning, many methods have been developed for hiding information in models to achieve covert communication. However, these methods are not applicable to federated learning, where model aggregation invalidates the exact information embedded in the model by the client. To address this problem, we propose a novel method for covert communication in federated learning based on the poisoning attack. Our approach achieves 100% accuracy in covert message transmission between two clients and is shown to be both stealthy and robust through extensive experiments. However, existing defense methods are limited in their effectiveness against our attack scheme, highlighting the urgent need for new protection methods to be developed. Our study emphasizes the necessity of research in covert communication and serves as a foundation for future research in federated learning attacks and defenses.

本研究提出了一种基于毒化攻击的联邦学习中隐蔽通信的新方法，可以实现100%的信息隐藏和传输，并证明了该方法的隐蔽和鲁棒性。然而，现有的防御方法对我们的攻击方案效果有限，强调了需要开发新的保护方法。该研究强调了在隐蔽通信方面的研究的必要性，并为联邦学习攻击和防御的未来研究奠定了基础。

基于中毒攻击的联邦学习隐蔽通信