Related Experiment Video
Updated: Sep 6, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
Published on: February 3, 2021
Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey
Abdul Rehman Javed1, Muhammad Abul Hassan2, Faisal Shahzad1
1Department of Cyber Security, Air University Islamabad, Islamabad 44000, Pakistan.
Federated Learning (FL) and blockchain integration addresses challenges in smart transport infrastructure (STI) by enhancing data privacy and security. This approach tackles big data issues in vehicular networks, improving overall system efficiency and trustworthiness.
Area of Science:
- Intelligent Transportation Systems
- Computer Science
- Network Security
Background:
- The Internet of Things (IoT) is crucial for Smart Transport Infrastructure (STI).
- Machine learning (ML) enhances STI but faces challenges like computation cost, communication overhead, and privacy concerns due to vast data from numerous components.
- Federated Learning (FL) and blockchain offer solutions to these challenges.
Purpose of the Study:
- To explore the integration of Federated Learning (FL) and blockchain in vehicular networks.
- To address privacy preservation and big data handling issues in STI management.
- To enhance data integrity and security in Smart Transport Infrastructure.
Main Methods:
- Survey of vehicular networks and STI.
- Detailed examination of Federated Learning (FL) and blockchain technologies.
- Analysis of FL and blockchain applications in Vehicular Ad Hoc Networks (VANETs) focusing on security and privacy.
Main Results:
- Federated Learning (FL) effectively addresses privacy preservation and big data challenges in STI.
- Blockchain provides data integrity and enhanced security for STI.
- FL and blockchain integration offers a robust solution for secure and efficient vehicular networks.
Conclusions:
- The integration of FL and blockchain is a promising approach for advancing secure and private STI.
- Further research is needed to overcome current challenges and explore future directions in this domain.
More Related Videos
07:49Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
Published on: November 26, 2019
08:04Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
Published on: April 23, 2020
Related Concept Videos
Issues And Trends In Healthcare Delivery System
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Introduction to Learning
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
Cognitive Learning
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Observational Learning
Distributed Loads: Problem Solving
Associative Learning
Classical conditioning, also known...