## 1 Introduction
### Background
In aerial networks, UAVs employ machine learning for autonomous navigation, mission planning, and real-time image processing for disaster monitoring. On the ground, machine learning supports traffic optimization, intelligent resource allocation, and user behavior prediction. However, implementing these machine learning applications in SAGINs introduces significant privacy and security concerns. For example, when multiple satellites collaboratively train models for weather prediction, sensitive observation data from military or commercial entities might be exposed during model updates. Similarly, UAVs collecting and processing emergency response imagery might inadvertently capture and transmit personally identifiable information. In ground networks, user mobility prediction models could leak sensitive location patterns if not properly protected during the learning process. These scenarios highlight how machine learning in SAGIN environments faces unique privacy challenges due to the heterogeneous and distributed nature of the network. Traditional centralized machine learning approaches would require raw data transmission across various network layers, creating multiple attack vectors for adversaries. Federated learning emerges as a promising solution by enabling collaborative model training without sharing raw data. However, even with federated learning, the SAGIN's architecture introduces additional privacy vulnerabilities: model updates transmitted through space-based nodes might be intercepted by unauthorized parties; the dynamic topology increases the risk of membership inference attacks; and the heterogeneous computational capabilities across different nodes can lead to uneven privacy protection levels. Therefore, a specialized privacy-preserving federated learning framework for SAGINs is essential to address these domain-specific challenges. In the modern network environment, data security and privacy protection have become critical issues. With the rapid development of digital technology, increasing amounts of data are being collected, stored, and processed [6]. These data often contain sensitive information, and privacy breaches can have serious consequences: for individuals, the leakage of personal privacy data may lead to identity theft, financial loss, and even personal safety risks; for enterprises, the leakage of business data may result in legal liabilities and damage to corporate reputation, causing economic losses. Evidently, privacy protection is particularly important in modern network environments. Due to its unique architecture and broad application scenarios, SAGINs face even more severe challenges in security and privacy protection: SAGINs integrate satellites, unmanned aerial vehicles, and ground stations, creating a complex and diverse communication environment that complicates communication paths, increases potential security vulnerabilities, and exposes privacy data to attacks during transmission. The mobility of satellites and unmanned aerial vehicles causes dynamic changes in network topology, challenging traditional security mechanisms and making it crucial to ensure continuous and effective privacy protection in dynamic network environments. SAGINs are widely used in military operations, emergency rescue, telemedicine, and other fields where the transmitted data often involve highly sensitive information related to national security, medical privacy, etc., making it a target for Advanced Persistent Threats (APTs) and other complex network attacks [7]. Therefore, achieving effective privacy protection in SAGINs is not only technically necessary but also an important guarantee for ensuring the reliability and sustainability of the network.