# A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles

arXiv: https://arxiv.org/abs/2203.05192 · HTML: https://mlsanity.com/paper/2203.05192/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 109 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 24 of 24)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach](https://mlsanity.com/paper/2003.08725/) (2020)
- [Energy Demand Prediction with Federated Learning for Electric Vehicle Networks](https://mlsanity.com/paper/1909.00907/) (2019)
- [GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning](https://mlsanity.com/paper/1909.00047/) (2019)
- [Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks](https://mlsanity.com/paper/2011.09902/) (2020)
- [Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach](https://mlsanity.com/paper/2004.03877/) (2020)
- [Fusion of Federated Learning and Industrial Internet of Things: A Survey](https://mlsanity.com/paper/2101.00798/) (2021)
- [Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms](https://mlsanity.com/paper/2007.12004/) (2020)
- [Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles](https://mlsanity.com/paper/2103.03786/) (2021)
- [UAV Communications for Sustainable Federated Learning](https://mlsanity.com/paper/2103.11073/) (2021)
- [Joint Auction-Coalition Formation Framework for Communication-Efficient Federated Learning in UAV-Enabled Internet of Vehicles](https://mlsanity.com/paper/2007.06378/) (2020)
- [FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing](https://mlsanity.com/paper/2110.12876/) (2021)
- [Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles](https://mlsanity.com/paper/2101.00191/) (2021)
- [A Blockchain based Federated Learning for Message Dissemination in Vehicular Networks](https://mlsanity.com/paper/2109.06667/) (2021)
- [Blockchain for the Internet of Vehicles towards Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2007.06022/) (2020)
- [Block4Forensic: An Integrated Lightweight Blockchain Framework for Forensics Applications of Connected Vehicles](https://mlsanity.com/paper/1802.00561/) (2018)
- [Privacy-Preserving Smart Parking System Using Blockchain and Private Information Retrieval](https://mlsanity.com/paper/1904.09703/) (2019)
- [Blockchain-Based Federated Learning in Mobile Edge Networks with Application in Internet of Vehicles](https://mlsanity.com/paper/2103.01116/) (2021)
- [Toward a Secure and Decentralized Blockchain-based Ride-Hailing Platform for Autonomous Vehicles](https://mlsanity.com/paper/1910.00715/) (2019)
- [Federated Learning Meets Contract Theory: Energy-Efficient Framework for Electric Vehicle Networks](https://mlsanity.com/paper/2004.01828/) (2020)

## Built on by (most cited first, 7 of 7)

- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems](https://mlsanity.com/paper/2309.11039/) (2023)
- [Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture Models](https://mlsanity.com/paper/2405.09903/) (2024)
- [Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain](https://mlsanity.com/paper/2409.16444/) (2024)
- [Centralized vs Decentralized Federated Learning: A trade-off performance analysis](https://mlsanity.com/paper/2605.16089/) (2026)
- [Blockchain-based Federated Learning for Decentralized Energy Management Systems](https://mlsanity.com/paper/2306.17186/) (2023)

## Abstract (arXiv)

While the convergence of Artificial Intelligence (AI) techniques with improved information technology systems ensured enormous benefits to the Internet of Vehicles (IoVs) systems, it also introduced an increased amount of security and privacy threats. To ensure the security of IoVs data, privacy preservation methodologies have gained significant attention in the literature. However, these strategies also need specific adjustments and modifications to cope with the advances in IoVs design. In the interim, Federated Learning (FL) has been proven as an emerging idea to protect IoVs data privacy and security. On the other hand, Blockchain technology is showing prominent possibilities with secured, dispersed, and auditable data recording and sharing schemes. In this paper, we present a comprehensive survey on the application and implementation of Blockchain-Enabled Federated Learning frameworks for IoVs. Besides, probable issues, challenges, solutions, and future research directions for BC-Enabled FL frameworks for IoVs are also presented. This survey can further be used as the basis for developing modern BC-Enabled FL solutions to resolve different data privacy issues and scenarios of IoVs.