Sathwik Narkedimilli, Amballa Venkata Sriram, Sujith Makam et al. · Indian Institute of Information Technology Dharwad · Vardhaman College of Engineering
Proposes an IoV federated learning framework defending against gradient leakage and model poisoning via dynamic masking, SMPC, and blockchain validation
Model Inversion AttackData Poisoning Attackfederated-learning
The FAPL-DM-BC solution is a new FL-based privacy, security, and scalability solution for the Internet of Vehicles (IoV). It leverages Federated Adaptive Privacy-Aware Learning (FAPL) and Dynamic Masking (DM) to learn and adaptively change privacy policies in response to changing data sensitivity and state in real-time, for the optimal privacy-utility tradeoff. Secure Logging and Verification, Blockchain-based provenance and decentralized validation, and Cloud Microservices Secure Aggregation using FedAvg (Federated Averaging) and Secure Multi-Party Computation (SMPC). Two-model feedback, driven by Model-Agnostic Explainable AI (XAI), certifies local predictions and explanations to drive it to the next level of efficiency. Combining local feedback with world knowledge through a weighted mean computation, FAPL-DM-BC assures federated learning that is secure, scalable, and interpretable. Self-driving cars, traffic management, and forecasting, vehicular network cybersecurity in real-time, and smart cities are a few possible applications of this integrated, privacy-safe, and high-performance IoV platform.
federatedIndian Institute of Information Technology Dharwad · Vardhaman College of Engineering
ML Security Papers