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

2025-01-02

AI Summary

Addressing the challenge of jointly ensuring security, privacy, and scalability in Internet of Vehicles (IoV), this paper proposes FAPL-DM—the first federated learning framework tailored for IoV. It synergistically integrates Federated Adaptive Privacy Learning (FAPL) with Dynamic Masking (DM), augmented by blockchain-based auditability, SMPC-secured model aggregation, and a dual-model Model-Agnostic eXplainable AI (XAI) feedback loop. The framework enables millisecond-level dynamic privacy tuning, tamper-proof model aggregation under zero-trust assumptions, and end-to-end global interpretability. Evaluated on real-world IoV scenarios, it achieves a 42% improvement in prediction interpretability and a 31% reduction in communication overhead over baseline methods. Its core innovations lie in a real-time privacy-utility trade-off mechanism and a tripartite federated architecture unifying verifiability, explainability, and scalability.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Interpretability, Explainability, and Transparency
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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.

Problem

• Security
• Privacy
• Scalability

Innovation

• Blockchain Integration
• Dynamic Privacy Adjustment
• Explainable AI (XAI)