SecFedDrive: Securing Federated Learning for Autonomous Driving Against Backdoor Attacks

Federated learning (FL) enables collaborative model training without sharing the private data of each individual participant, making it well-suited for autonomous driving applications. Preserving the integrity of sensitive data is crucial for the security of these systems due to the direct implications for passenger safety. Although federated model training enhances data privacy for individual participants, it remains vulnerable to stealthy backdoor attacks that can alter the global model, potentially threatening system reliability in high-stakes scenarios such as autonomous driving. We introduce SecFedDrive, a robust defense mechanism against backdoor attacks on a decentralized deep FL system. Our approach reduces attack success rates to as low as 2%, significantly enhancing the security and reliability of FL models in autonomous driving environments, and demonstrates significant reductions in training time relative to a comparable architecture.