mlsanityData to 14 Oct 2026

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

arXiv 2203.05192 ↗First posted 10 Mar 2022cs.CR cs.AIcited by 7

Not on the map. Why?

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.

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

24 of 109 references.

References · 24

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Towards Federated Learning at Scale: System Designcited by 832
  3. 2019Federated Machine Learning: Concept and Applicationscited by 412
  4. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  5. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  6. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 83
  7. 2019Energy Demand Prediction with Federated Learning for Electric Vehicle Networkscited by 46
  8. 2019GADMM: Fast and Communication Efficient Framework for Distributed Machine Learningcited by 30
  9. 2020Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networkscited by 30
  10. 2020Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approachcited by 29
  11. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 29
  12. 2020Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarmscited by 26
12 more
  1. 2021Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehiclescited by 24
  2. 2021UAV Communications for Sustainable Federated Learningcited by 15
  3. 2020Joint Auction-Coalition Formation Framework for Communication-Efficient Federated Learning in UAV-Enabled Internet of Vehiclescited by 14
  4. 2021FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computingcited by 11
  5. 2021Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehiclescited by 7
  6. 2021A Blockchain based Federated Learning for Message Dissemination in Vehicular Networkscited by 7
  7. 2020Blockchain for the Internet of Vehicles towards Intelligent Transportation Systems: A Surveycited by 6
  8. 2018Block4Forensic: An Integrated Lightweight Blockchain Framework for Forensics Applications of Connected Vehiclescited by 5
  9. 2019Privacy-Preserving Smart Parking System Using Blockchain and Private Information Retrievalcited by 3
  10. 2021Blockchain-Based Federated Learning in Mobile Edge Networks with Application in Internet of Vehiclescited by 3
  11. 2019Toward a Secure and Decentralized Blockchain-based Ride-Hailing Platform for Autonomous Vehiclescited by 1
  12. 2020Federated Learning Meets Contract Theory: Energy-Efficient Framework for Electric Vehicle Networkscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 2; 2025: 0; 2026: 1.

7 papers.

  1. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 138
  2. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 40
  3. 2023Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problemscited by 15
  4. 2024Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture Modelscited by 2
  5. 2024Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchaincited by 2
  6. 2026Centralized vs Decentralized Federated Learning: A trade-off performance analysiscited by 1
  7. 2023Blockchain-based Federated Learning for Decentralized Energy Management Systemscited by 0