FAPL-DM-BC: A Secure and Scalable FL Framework with Adaptive Privacy and Dynamic Masking, Blockchain, and XAI for the IoVs

TL;DR

This paper introduces FAPL-DM-BC, a comprehensive federated learning framework for IoV that enhances privacy, security, scalability, and interpretability through adaptive privacy policies, blockchain, and explainable AI.

Contribution

It proposes a novel integrated framework combining adaptive privacy, blockchain-based security, and XAI for secure and scalable IoV applications.

Findings

Achieves real-time adaptive privacy policy adjustments.

Ensures secure, decentralized validation via blockchain.

Provides interpretable predictions with XAI feedback.

Abstract

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.