Friday 28 February 2025
The Internet of Vehicles (IoV) is a rapidly growing network of connected cars, infrastructure, and devices that promises to revolutionize transportation systems around the world. But as more vehicles hit the roads, concerns about privacy, security, and scalability are rising. To address these issues, researchers have developed a new framework for federated learning, a technique that enables multiple parties to collaboratively train machine learning models without sharing their individual data.
The proposed framework, called FAPL-DM-BC, combines several innovative technologies to create a robust and scalable solution for IoV. Federated adaptive privacy-aware learning (FAPL) allows the system to adaptively adjust its privacy controls in real-time based on changing environmental conditions and data sensitivity. Dynamic masking (DM) ensures that individual devices’ data remains private by obscuring their transmission patterns. Blockchain technology provides a secure and decentralized logging mechanism, while model-agnostic explainable AI (XAI) enables local predictions and explanations.
The FAPL-DM-BC framework is designed to address the unique challenges of IoV, including data leakage, model poisoning, and real-time processing inefficiencies. It uses cloud microservices for scalability and modularity, allowing it to handle large-scale deployments without compromising performance. The system also incorporates secure multi-party computation (SMPC) for aggregated model updates, ensuring that individual devices’ data remains private.
One of the key benefits of FAPL-DM-BC is its ability to provide real-time decision-making and predictive functions for IoV applications such as adaptive traffic control or swarm-based automated vehicle control. By integrating XAI with federated learning, the system can explain local predictions and improve model reliability, making it more suitable for high-stakes applications.
The FAPL-DM-BC framework has significant implications for the development of IoV systems, enabling secure, scalable, and interpretable machine learning models that can be deployed in real-world scenarios. As the number of connected vehicles on the roads continues to grow, the need for robust and reliable solutions like this framework becomes increasingly pressing.
In addition to its technical merits, FAPL-DM-BC also has important societal implications. By enabling secure and private data sharing among vehicles and infrastructure, it can help reduce traffic congestion, improve road safety, and enhance overall transportation efficiency. As IoV technology continues to evolve, frameworks like this one will play a crucial role in shaping the future of mobility and transportation systems around the world.
Cite this article: “Federated Learning Framework for Secure and Scalable IoV Applications”, The Science Archive, 2025.
Internet Of Vehicles, Federated Learning, Machine Learning, Privacy, Security, Scalability, Iov, Blockchain, Explainable Ai, Secure Multi-Party Computation
