Heterogeneous Federated Deep Reinforcement Learning-Empowered Dual-Threat Jamming Detection in Space-Air-Ground ISAC Networks

Keyphrases

• Aggregation Mechanism 33%
• Aggregator 33%
• Challenges Faced 33%
• Communication Device 33%
• Communication Networks 100%
• Communication Performance 33%
• Communication Signal 33%
• Critical Layers 33%
• Data Privacy 33%
• Deep Q-learning 66%
• Distributed Environment 33%
• Distributed Heterogeneous Data 33%
• Distributed Patterns 33%
• Distributed Training 33%
• Distribution Feature 33%
• Dynamic Weight 33%
• Dynamically Adjust 33%
• Federated Deep Reinforcement Learning 100%
• Federated Learning 66%
• Function Features 33%
• Heterogeneous Nature 33%
• Identically Distributed 66%
• Integrated Communication 100%
• Integrated Sensing 100%
• ISAC System 100%
• Jammer 100%
• Jamming Conditions 33%
• Jamming Detection 100%
• Learning-based 33%
• Local Model 33%
• Machine Learning Approach 33%
• Model Updating 33%
• Network Performance 33%
• Network Security 33%
• Numerical Results 33%
• Privacy-preserving 33%
• Reinforcement Learning Framework 66%
• Resilient 66%
• Sensing Devices 33%
• Sensing Network 100%
• Sensing Performance 33%
• Sensing Signal 33%
• Severe Degradation 33%
• Space-air-ground 100%
• Space-air-ground Integrated 100%
• Spectral Correlation Function 33%
• Time-frequency Distribution 33%
• Traditional Machine Learning 33%
• User Equipment 33%
• Vision Transformer Model 66%
• Weighted Aggregation 33%

Computer Science

• Collected Data 33%
• Communication Device 33%
• Communication Network 100%
• Data Privacy 33%
• Deep Reinforcement Learning 100%
• Distributed Environment 33%
• Federated machine learning 66%
• Heterogeneous Data 33%
• Learning Framework 66%
• Machine Learning Approach 33%
• Network Performance 33%
• Privacy Preserving 33%
• Protecting Data 33%
• time-frequency distribution 33%
• Transformer Model 66%
• Unmanned Aerial Vehicle 33%
• Vision Transformer 66%