## 2 Related Work
### Federated Learning  
Federated Learning (FL), as an emerging distributed machine learning paradigm, allows models to be trained across multiple devices or nodes, uploading only model updates to a central server for aggregation, thereby effectively reducing communication overhead and protecting data privacy [15]. Early research primarily focused on FL architecture design and basic algorithm proposals. [PERSON] et al. [16] proposed the classic Federated Averaging (FedAvg) algorithm, which optimizes the global model by averaging local updates in each training round. Privacy protection has always been one of the core issues in federated learning research. To prevent sensitive information leakage through model updates, researchers have introduced various privacy protection techniques. [PERSON] et al. [17] proposed a differential privacy federated learning method that guarantees data privacy while still enabling effective joint learning by adding noise to each model update. Communication efficiency is an important challenge for FL, especially in network environments with limited bandwidth or high latency. To address this issue, many studies have proposed efficient model update and aggregation methods. [PERSON] et al. [18] proposed a communication-efficient federated learning method based on model compression, addressing downstream communication through server model compression techniques and enhancing upstream communication efficiency through client-side model compression. Data heterogeneity (Non-IID) and system heterogeneity represent another major challenge for FL. Inconsistent data distribution among participants can prevent traditional FL from effectively training a globally unified model. Addressing data heterogeneity, [PERSON] et al. [19] proposed a Clustered Federated Learning (CFL) framework, which enhances model training effectiveness by grouping participants to ensure consistent data distribution within each group. For system heterogeneity, [PERSON] et al. [20] proposed an Efficient Asynchronous Federated Learning (EAFL) framework based on clustering and two-stage aggregation, achieving better learning performance and higher efficiency in heterogeneous edge environments. As federated learning becomes more widespread, security issues have become increasingly important. To enhance the security of FL systems, researchers have proposed various defense mechanisms against different types of attacks. For example, model poisoning attacks, where malicious clients deliberately send corrupted model updates to compromise the global model, have been addressed by [PERSON] et al. [21], who proposed a lightweight defense mechanism (OUTPUTOST) for federated learning, providing sufficient adaptive protection throughout the federated learning process. Other security threats include inference attacks such as membership inference attacks, where adversaries attempt to determine if specific data samples were used for training, as demonstrated by [PERSON] et al. [22], who showed that such attacks can succeed even in the federated setting. Data reconstruction attacks represent another significant threat, where adversaries attempt to reconstruct private training data from shared gradients, as investigated by [PERSON] et al. [23], who proposed novel techniques to mitigate such vulnerabilities. Additionally, backdoor attacks have emerged as a concerning threat, where adversaries inject hidden functionalities that are activated only with specific triggers, as studied by [PERSON] et al. [24], who demonstrated how such attacks can persist even with robust aggregation mechanisms. With satellite communication technology advancement, federated learning applications in satellite networks have gained increasing attention. [PERSON] et al. [25] proposed a Beam Hopping technique that illuminates only selected beams at specific times, enabling flexible adaptation to traffic demands.