## 2 Related Work
### Privacy Protection  
Privacy protection has become an increasingly important research direction in modern communication and data analysis systems, with significant applications in distributed machine learning and federated learning. With the frequent occurrence of data privacy breach incidents and the implementation of relevant laws and regulations, researchers have explored how to effectively protect data privacy at different levels. Particularly in distributed systems, how to conduct effective data analysis without exposing users' sensitive data has become a current research hotspot. Differential Privacy (DP) is an important technology in the field of privacy protection, which protects data privacy by adding noise during the data analysis process. Differential privacy ensures that through operations on data, external observers cannot determine whether a specific data point exists in the dataset. [PERSON] et al. [27] first proposed the concept of differential privacy and provided a rigorous mathematical definition for it. In federated learning, the application of differential privacy typically involves adding noise to gradients or parameters during the model update phase. [PERSON] et al. [17] proposed a differential privacy-based federated learning method that protects participants' data privacy by adding noise to model updates in each training cycle. Homomorphic Encryption (HE) [28] is an encryption method that allows computations to be performed on encrypted data. The advantage of homomorphic encryption in privacy protection is that it allows data to be stored in an encrypted state, thereby ensuring data privacy. Researchers have proposed schemes that combine homomorphic encryption with federated learning to conduct model training while ensuring data privacy. Addressing the high computational and communication costs in cross-silo federated learning, [PERSON] et al. [29] proposed an efficient homomorphic encryption federated learning method (BatchNormCrypt) that significantly reduces encryption and communication overhead through batch gradient encryption. Due to the high computational overhead of homomorphic encryption, practical applications still face challenges in computational efficiency and resource consumption. Secure Multi-party Computation (MPC) [30] is another common privacy protection technology that allows multiple parties to jointly compute the result of a function without revealing their respective data. The core idea of MPC is to complete data computation through multi-party collaboration while ensuring the privacy of all parties. In federated learning, MPC is used to protect model parameters and gradients exchanged between nodes. [PERSON] et al. [31] proposed a two-phase multi-party computation scheme to achieve privacy-preserving model aggregation in federated learning, ensuring the privacy and security of information exchanged between participating clients. Although secure multi-party computation has good privacy protection capabilities in some applications, it also faces higher computational complexity. In addition to differential privacy, homomorphic encryption, and MPC, other technologies have also been applied to privacy protection in federated learning. For example, privacy protection methods based on Generative Adversarial Networks (GAN), where [PERSON] et al. [32] simulated data distribution through generative models to produce pseudo-data similar to real data, enabling training while ensuring privacy. Although many studies in privacy-preserving federated learning have proposed differential privacy, homomorphic encryption, and secure multi-party computation methods, providing strong guarantees for data privacy protection, existing privacy protection methods still have several deficiencies in environments with multiple challenges such as high latency, limited bandwidth, dynamic network topology, data heterogeneity, and computational resource heterogeneity, as found in Space-Air-Ground Integrated Networks.