Federated learning meets recursive self-distillation: A scalable malware detection framework for IoVs

Latif, Shahid; Louadj, Rania; Djenouri, Djamel

Abstract

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 model generalization, mitigates overfitting to non-IID data, and improves adaptability to new malware variants. The RSD process iteratively transfers knowledge from a teacher model to a lightweight student model, reducing model complexity and communication overhead while preserving detection accuracy. Experimental results have confirmed that FL-RSD achieves significant performance improvements over baseline models in terms of malware detection accuracy and adversarial robustness. FL-RSD attains a malware detection accuracy and an average adversarial robustness scores over 92%, outperforming Federated Proximal, FedNova, and Hierarchical FL. The improvements range from 3% to 48% for detection accuracy, and 6% to 81% for the adversarial robustness score. Additionally, FL-RSD demonstrates a minimal memory footprint with a final global model size of 373.17 KB and maintains knowledge retention with an Average Catastrophic Forgetting Score of 93.66%. These results confirm that FL-RSD offers a lightweight, efficient, and scalable solution for malware detection in IoV.

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: 2025 IEEE 101st Vehicular Technology Conference: VTC2025-Spring
• Start Date: Jun 17, 2025
• End Date: Jun 20, 2025
• Acceptance Date: Apr 24, 2025
• Deposit Date: Apr 25, 2025
• Peer Reviewed: Peer Reviewed
• Keywords: Index Terms-Cybersecurity; Internet of Vehicles; Federated Learning; Malware Detection
• Public URL: https://uwe-repository.worktribe.com/output/14326990