Federated Learning-Driven Covert Communication in Satellite–Terrestrial Integrated Networks: A Privacy-Preserving Framework

Due to the broadcasting characteristics of satellite-terrestrial integrated networks (STINs), security vulnerabilities have emerged as a critical concern requiring urgent mitigation strategies. Unlike traditional security methods, federated learning (FL) enables a large number of participants to collaborate without disclosing actual privacy data. Its potential as a framework that combines collaborative model training and covert payload transmission in STINs represents a significant research gap. This paper proposes FedSAT, a novel FL-based covert communication scheme for STINs, in which each participant in the FL process can utilize the shared learning protocol as a covert medium for transmitting arbitrary information in privacy-preserving framework. Our framework leverages the dual capabilities of FL for collaborative model training and covert payload embedding, utilizing Geostationary Earth Orbit (GEO) satellites and distributed terrestrial nodes to embed sensitive data within FL parameter updates. The system maintains model convergence accuracy while implementing strategic encryption to achieve robust sharing and transmission of payloads within the FL framework. Comprehensive simulation tests demonstrate the framework significant efficacy, achieving a 98.7% communication coverage for covert payload transmission under monitoring by low Earth orbit (LEO) surveillance satellites, with only a 0.8% decrease in model accuracy. This breakthrough achievement paves the way for a transformative paradigm in covert cross-domain communication for next-generation networks.