mlsanityData to 14 Oct 2026

Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systems

arXiv 2102.10142 ↗First posted 19 Feb 2021cs.NI cs.LGcited by 10

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Abstract (arXiv)

With the incoming introduction of 5G networks and the advancement in technologies, such as Network Function Virtualization and Software Defined Networking, new and emerging networking technologies and use cases are taking shape. One such technology is the Internet of Vehicles (IoV), which describes an interconnected system of vehicles and infrastructure. Coupled with recent developments in artificial intelligence and machine learning, the IoV is transformed into an Intelligent Transportation System (ITS). There are, however, several operational considerations that hinder the adoption of ITS systems, including scalability, high availability, and data privacy. To address these challenges, Federated Learning, a collaborative and distributed intelligence technique, is suggested. Through an ITS case study, the ability of a federated model deployed on roadside infrastructure throughout the network to recover from faults by leveraging group intelligence while reducing recovery time and restoring acceptable system performance is highlighted. With a multitude of use cases and benefits, Federated Learning is a key enabler for ITS and is poised to achieve widespread implementation in 5G and beyond networks and applications.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 5 of its 16 references that are in the map. First posted on arXiv 19 Feb 2021.

Builds on

5 of 16 references.

References · 5

  1. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  2. 2014NFV: State of the Art, Challenges and Implementation in Next Generation Mobile Networks (vEPC)cited by 3
  3. 2020Machine Learning for Performance-Aware Virtual Network Function Placementcited by 3
  4. 2020The Need for Advanced Intelligence in NFV Management and Orchestrationcited by 3
  5. 2020A Machine Learning-Based Migration Strategy for Virtual Network Function Instancescited by 1

Built on by

Cited by year

Citing papers per year: 2021: 3; 2022: 1; 2023: 3; 2024: 3.

10 papers.

  1. 2021PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streamscited by 17
  2. 2022IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspectivecited by 16
  3. 2023Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problemscited by 15
  4. 2021Concept Drift Detection in Federated Networked Systemscited by 10
  5. 2021Byzantine Fault-Tolerance in Federated Local SGD under 2f-Redundancycited by 7
  6. 2024Hierarchical Federated Learning in Multi-hop Cluster-Based VANETscited by 7
  7. 2023Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocationcited by 5
  8. 2024FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of Vehiclescited by 2
  9. 2023Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehiclescited by 0
  10. 2024Distributed client selection with multi-objective in federated learning assisted Internet of Vehiclescited by 0