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

arXiv: https://arxiv.org/abs/2501.01063 · HTML: https://mlsanity.com/paper/2501.01063/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2025.

## Built on by (most cited first, 2 of 2)

- [AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space](https://mlsanity.com/paper/2506.22487/) (2025)
- [A Novel XAI-Enhanced Quantum Adversarial Networks for Velocity Dispersion Modeling in MaNGA Galaxies](https://mlsanity.com/paper/2510.24598/) (2025)

## Abstract (arXiv)

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.