Global Aggregation Node Selection Scheme in Federated Learning for Vehicular Ad Hoc Networks (VANETs)

Zouheir Trabelsi, Tariq Qayyum, Kadhim Hayawi, Muhammad Ali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Federated learning allows multiple users and parties to collaborate and train machine learning models in a distributed and privacy-preserving manner in Vehicular Adhoc Networks VANETs. This computing paradigm addresses privacy concerns; however, it comes at a considerable cost of network resources. After training the machine learning models in conventional federated learning frameworks, devices share that model with a central server, mostly cloud, where the global aggregation is performed. Multiple devices communicating with a central server raise network bandwidth and congestion concerns. To solve this problem, we proposed a federated learning framework for VANETs where instead of using a fixed global aggregator, we used variable global aggregation nodes. The global aggregation node is selected based on communication delay and workload in the proposed framework. We also believe that, in a vehicular Adhoc network, all network nodes cannot participate in the learning process due to network, computation, and energy resource limitations. We Also proposed a client selection algorithm that adapts itself and selects some clients based on specific criteria. Finally, the proposed technique is compared with the hierarchical federated learning framework (HFL) and FedAvg where proposed method outperformed in terms of accuracy.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665483568
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2022 - Barcelona, Spain
Duration: Aug 1 2022Aug 3 2022

Publication series

Name2022 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2022

Conference

Conference2022 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2022
Country/TerritorySpain
CityBarcelona
Period8/1/228/3/22

Keywords

  • Federated Learning
  • Fog computing
  • IoT
  • Sensors,Omnet++
  • VANET
  • Veins

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Information Systems and Management
  • Control and Optimization

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