Privacy and security

A hybrid privacy-preserving algorithm combining Pre-Aggregation Similarity Measurement (PA-SM) and Differential Privacy (DP) enhances the security and resilience of Federated Learning (FL) in Intelligent Transportation Systems (ITS) by defending against adversarial attacks while maintaining model performance.

A hybrid privacy-preserving algorithm combining Pre-Aggregation Similarity Measurement (PA-SM) and Differential Privacy (DP) enhances the security and resilience of Federated Learning (FL) in Intelligent Transportation Systems (ITS) by defending against adversarial attacks while maintaining model performance.

This work presents an empirical evaluation of secure federated learning techniques for autonomous vehicle applications. Using the LISA traffic light dataset, it compares secure aggregation and multiparty computation methods against poisoning and inference attacks, demonstrating how privacy-preserving aggregation can improve the resilience of federated learning systems during both training and inference.