ZeroTKS: Zero-trust Knowledge Synchronization via Federated Fine-tuning for Secure Semantic Communications

Long Li, Gaolei Li, Shuilin Li, Rui Xu, Jianhua Li

Abstract: Semantic communication has experienced considerable growth and advancement due to its potential to support future intelligent applications (e.g., augmented reality). The realization of the above potential superiority depends on the construction and synchronization of semantic knowledge base among multiple ends. However, existing methods for constructing semantic knowledge base fail to adhere to the zero-trust architecture, where all communication ends can act as knowledge contributors without rigorous authentication. Motivated by this insight, we propose a novel zero-trust knowledge synchronization (ZeroTKS) scheme for secure semantic communication based on federated fine-tuning. In the proposed scheme, we firstly explore to introduce homomorphic encryption into federated fine-tuning of large models to securely synchronize the semantic knowledge base against privacy leakage risks. And also, to prevent from malicious model tampering attacks, a novel Age-of-Update-based access control mechanism is designated, in which only nodes with high AoU values can be authorized to participate in updating the semantic knowledge base by combining with hash-based message authentication code. Extensive experiments based on four different datasets in the GLUE benchmark show that our proposed scheme can securely synchronize distributed semantic knowledge base with maintaining acceptable performance.