Federated Deep Learning Model for False Data Injection Attack Detection in Cyber Physical Power Systems
Cyber-physical power systems (CPPS) integrate information and communication technology into conventional electric power systems to facilitate bidirectional communication of information and electric power between users and power grids. Despite its benefits, the open communication environment of CPPS...
        Saved in:
      
    
          | Main Authors: | Firdous Kausar, Sambrdhi Deo, Sajid Hussain, Zia Ul Haque | 
|---|---|
| Format: | Article | 
| Language: | English | 
| Published: | MDPI AG
    
        2024-10-01 | 
| Series: | Energies | 
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/17/21/5337 | 
| Tags: | Add Tag 
      No Tags, Be the first to tag this record!
   | 
Similar Items
- 
                
                    A survey on federated learning in crowd intelligence        
                          
 by: Qiang YANG, et al.
 Published: (2022-03-01)
- 
                
                    A semi-synchronous federated learning framework with chaos-based encryption for enhanced security in medical image sharing        
                          
 by: Animesh Roy, et al.
 Published: (2025-03-01)
- 
                
                    Cybersecurity in Smart Grids: Detecting False Data Injection Attacks Utilizing Supervised Machine Learning Techniques        
                          
 by: Anwer Shees, et al.
 Published: (2024-11-01)
- 
                
                    Privacy Preserving Machine Learning With Federated Personalized Learning in Artificially Generated Environment        
                          
 by: Md. Tanzib Hosain, et al.
 Published: (2024-01-01)
- 
                
                    Crowdsourced federated learning architecture with personalized privacy preservation        
                          
 by: Yunfan Xu, et al.
 Published: (2024-09-01)
 
       