Abstract: Federated Learning (FL) is designed to solve the irreconcilable contradiction between data sharing requirements and privacy needs. As a kind of distributed machine learning, FL needs to exchange a large number of model parameters between participants and the central server, which leads to a large amount of data communication. The iterative process of FL model updating depends on distributed data transmission, and once the transmission channel is located, its model data security and integrity will be difficult to guarantee. In this paper, a hybrid Covert Storage-Timing Channel (CSTC) scheme for FL is proposed. The secret message is firstly split into parallel-distributed coding units, and the secret data communication is achieved via adjusting the inter-packet delays to indicate which block is to be transmitted, and the overt traffic’s packet payload is selectively replaced with secret blocks according to the payload content. Thus, the position indicator of a secret block is embedded in both the time and storage features of the overt traffic, while the feature-location correspondence is pre-shared by the receiver and sender, and the adversary cannot grasp a secret message unless all features locating the secret block are obtained. Moreover, three variants of the original CSTC are proposed to fulfill the different performance requirements, and the experiments show that the undetectability and capacity of the proposed schemes are reasonable.

Key words: federated learning, covert channel, secret message, packet delay, undetectability

摘要： 联邦学习（Federated Learning，FL）旨在解决数据共享需求与隐私保护之间的矛盾。作为一种分布式机器学习方法，联邦学习需要在参与者与中央服务器之间交换大量的模型参数，这导致了大量数据通信。联邦学习模型更新的迭代过程依赖于分布式数据传输，一旦传输通道被定位，其模型数据的安全性和完整性就难以得到保障。本文提出了一种用于联邦学习的混合隐蔽时间存储隐蔽信道方案。首先，将秘密消息分割为并行分布式编码单元，通过调整分组延迟来指示要传输的块，并根据有效载荷内容有选择地用秘密块替换公开流量的分组有效载荷。因此，秘密块的位置指示器被嵌入正常流量的时间和存储特征中，而特征与位置对应关系由接收方和发送方预先共享，攻击者除非获取定位秘密块的所有特征，否则无法掌握秘密消息。此外，本文还提出了原始方案的3种变体，以满足不同的性能要求。在抗检测性和容量方面对上述所有方案进行了实验验证。

关键词: 联邦学习, 隐蔽通道, 秘密块, 分组延迟, 抗检测性