An integrated approach to mitigate poisoning attacks in federated learning frameworks

Latif, Shahid; Djenouri, Djamel; Adamatzky, Andrew

All Outputs (4)

Federated learning meets recursive self-distillation: A scalable malware detection framework for IoVs (2025)
Presentation / Conference Contribution

This paper proposes an integrated approach called FL-RSD, leveraging the key advantages of Federated Learning (FL) and Recursive Self-Distillation (RSD) for malware detection in the Internet of Vehicles (IoV). The proposed FL-RSD framework enhances m... Read More about Federated learning meets recursive self-distillation: A scalable malware detection framework for IoVs.

ARUC-GAN: A CycleGAN-based attention residual U-Net for low-dose CT denoising in smart healthcare (2025)
Journal Article

Low-dose computed tomography (LDCT) is increasingly adopted in medical imaging to minimize radiation exposure. However, the diagnostic accuracy is primarily affected by different noise sources, e.g., quantum, electronic, and reconstruction. Moreover,... Read More about ARUC-GAN: A CycleGAN-based attention residual U-Net for low-dose CT denoising in smart healthcare.

Federated learning in IoT environments: Examining the three-way see-saw for privacy, model-performance, and network-efficiency (2025)
Journal Article

This survey paper provides an in-depth exploration of Federated Learning (FL) in Internet of Things (IoT) environments , focusing on privacy-preserving techniques and their influence on model performance and network efficiency. It highlights key chal... Read More about Federated learning in IoT environments: Examining the three-way see-saw for privacy, model-performance, and network-efficiency.

An integrated approach to mitigate poisoning attacks in federated learning frameworks (2025)
Presentation / Conference Contribution

While Federated learning (FL) is considered privacy-preserving by nature, it remains vulnerable to many attacks, such as data and model poisoning, that compromise data integrity and model accuracy. Conventional privacy-preserving federated learning (... Read More about An integrated approach to mitigate poisoning attacks in federated learning frameworks.