A Federated Learning–Enabled Secure and Scalable SDN Framework for Energy‐Efficient VANETs

ABSTRACT Vehicular ad hoc networks (VANETs) play a central role in intelligent transportation systems (ITSs), yet their performance is often constrained by high mobility, limited scalability, privacy risks, and increasing security threats, including quantum‐enabled attacks. Existing approaches typically integrate software‐defined networking (SDN), federated learning (FL), blockchain, or cryptographic techniques in isolation, resulting in fragmented control and inconsistent security guarantees. To overcome these limitations, this work proposes FLEDGE‐SDVN , a unified FL‐enabled, blockchain‐secured, and postquantum‐resilient SDN framework for secure and energy‐efficient VANET operation. The framework employs dynamic hierarchical clustering for stable topology management and incorporates an FL‐based trust model to detect malicious vehicles without sharing raw data. A lightweight blockchain ensures tamper‐resistant maintenance of trust records, while Kyber‐ and Dilithium‐based postquantum cryptography provides long‐term secure authentication. SDN controllers enforce global routing policies, optimize traffic flow, and support adaptive decision‐making. Extensive NS‐3 and SUMO simulations demonstrate that FLEDGE‐SDVN significantly enhances network performance compared to CRAS‐FL, EECT, and DistB‐VNET. Specifically, the framework improves packet delivery ratio by 10%–22%, reduces end‐to‐end delay by 13%–28%, enhances energy efficiency by 17%–31%, and achieves trust accuracy up to 96%. These results confirm that FLEDGE‐SDVN offers a scalable, secure, and future‐ready solution for next‐generation vehicular communication systems.