Privacy-Preserving Federated Learning for Space–Air–Ground Integrated Networks: A Bi-Level Reinforcement Learning and Adaptive Transfer Learning Optimization Framework
Abstract
:1. Introduction
1.1. Background
1.2. Motivation and Contributions
2. Related Work
2.1. Space–Air–Ground Integrated Network
2.2. Federated Learning
Federated Learning Applications in Satellite Networks
2.3. Privacy Protection
3. Federated Learning for Space–Air–Ground Integrated Networks
3.1. Space–Air–Ground Integrated Network Architecture
3.2. Federated Learning Framework for Space–Air–Ground Integrated Networks
3.3. Adaptive Transfer Learning Strategy
3.4. Device Selection Optimization Strategy Based on Hierarchical Reinforcement Learning
3.4.1. Hierarchical Reinforcement Learning Strategy
3.4.2. Hierarchical Attention Mechanism
3.4.3. Meta-Learning-Based Strategy Optimization
3.5. Privacy-Preserving Federated Learning
3.5.1. Local Training at Terminal Nodes
3.5.2. Local Aggregation at Edge Nodes
3.5.3. Global Model Aggregation at the Central Server
4. Experimental Evaluation
4.1. Experimental Setup
4.1.1. Experimental Environment
4.1.2. Datasets
4.2. Experimental Results and Analysis
4.2.1. Performance Metric Evaluation Based on Four Datasets
4.2.2. Time Overhead Evaluation
4.2.3. Privacy Protection Evaluation
4.2.4. Ablation Study
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
- ├── main.py # Main entry point for experiments
- ├── config/
- │ └── config.yaml # Configuration parameters
- ├── models/
- │ ├── neural_networks.py # Neural network model definitions
- │ └── federated_models.py # Federated learning model implementations
- ├── algorithms/
- │ ├── adaptive_transfer.py # Adaptive transfer learning implementation
- │ ├── hierarchical_rl.py # Hierarchical reinforcement learning
- │ ├── privacy_budget.py # Privacy budget allocation
- │ └── robust_aggregation.py # Robust aggregation methods
- ├── utils/
- │ ├── data_loader.py # Dataset loading and preprocessing
- │ ├── metrics.py # Evaluation metrics
- │ └── visualization.py # Results visualization
- └── simulation/
- ├── network_topology.py # SAGIN network topology simulator
- ├── device_manager.py # Device status and capability simulator
- Detailed Code Structure Introduction
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Parameter | Value/Range | Description |
---|---|---|
LEO: Circular orbit with 90-min period | ||
MEO: Circular orbit with 6-h period | ||
Node Mobility Pattern | Varied | GEO: Fixed relative to Earth |
UAV: Random waypoint model at 100–300 m | ||
HAPS: Fixed position at 20 km altitude | ||
LEO–Ground: 5–15 min | ||
MEO–Ground: 20–40 min | ||
Connection Duration(min) | 5—Continuous | GEO–Ground: Continuous |
UAV–Ground: 10–30 min | ||
HAPS–Ground: Continuous | ||
LEO visibility changes: Every 5–15 min | ||
Topology Change Frequency | Varied | UAV connectivity changes: Every 10–30 min |
Ground device mobility: Every 30–60 min | ||
Space nodes: 10–50% | ||
Disconnection Probability | 0–50% | Air nodes: 5–30% |
(per hour) | Ground nodes: 0–10% |
Method | Accuracy | Precision | Recall | F1-Score | Average Loss |
---|---|---|---|---|---|
PPFL-SAGIN (dynamic weighting) | 0.9502 | 0.9534 | 0.9502 | 0.9512 | 0.1204 |
Static weighting strategy | 0.8512 | 0.8567 | 0.8512 | 0.8525 | 0.2695 |
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Li, L.; Zhu, L.; Li, W. Privacy-Preserving Federated Learning for Space–Air–Ground Integrated Networks: A Bi-Level Reinforcement Learning and Adaptive Transfer Learning Optimization Framework. Sensors 2025, 25, 2828. https://doi.org/10.3390/s25092828
Li L, Zhu L, Li W. Privacy-Preserving Federated Learning for Space–Air–Ground Integrated Networks: A Bi-Level Reinforcement Learning and Adaptive Transfer Learning Optimization Framework. Sensors. 2025; 25(9):2828. https://doi.org/10.3390/s25092828
Chicago/Turabian StyleLi, Ling, Lidong Zhu, and Weibang Li. 2025. "Privacy-Preserving Federated Learning for Space–Air–Ground Integrated Networks: A Bi-Level Reinforcement Learning and Adaptive Transfer Learning Optimization Framework" Sensors 25, no. 9: 2828. https://doi.org/10.3390/s25092828
APA StyleLi, L., Zhu, L., & Li, W. (2025). Privacy-Preserving Federated Learning for Space–Air–Ground Integrated Networks: A Bi-Level Reinforcement Learning and Adaptive Transfer Learning Optimization Framework. Sensors, 25(9), 2828. https://doi.org/10.3390/s25092828