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.

翻译：隐蔽通信已成为计算机安全领域的重要研究方向。它涉及将特定信息隐藏于载体中进行消息传输，常被用于传输私密数据、军事机密甚至恶意软件。在深度学习中，已有多种方法通过在模型中隐藏信息实现隐蔽通信。然而，这些方法不适用于联邦学习——因为模型聚合会使客户端嵌入模型的精确信息失效。为解决该问题，我们提出一种基于投毒攻击的联邦学习隐蔽通信新方法。该方法能在两个客户端之间实现100%准确率的隐蔽消息传输，并通过大量实验证明其兼具隐蔽性与鲁棒性。然而，现有防御方法对我们的攻击方案效果有限，这凸显了开发新型防护方法的迫切性。本研究强调了隐蔽通信研究的必要性，并为未来联邦学习攻击与防御研究奠定了基础。