mlsanityData to 7 Oct 2026

Distributed client selection with multi-objective in federated learning assisted Internet of Vehicles

arXiv 2401.03159 ↗First posted 6 Jan 2024cs.LG cs.DCcited by 0

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Abstract (arXiv)

Federated learning is an emerging distributed machine learning framework in the Internet of Vehicles (IoV). In IoV, millions of vehicles are willing to train the model to share their knowledge. Maintaining an active state means the participants must update their state to the FL server in a fixed interval and participate to next round. However, the cost by maintaining an active state is very large when there are a huge number of participating vehicles. In this paper, we proposed a distributed client selection scheme to reduce the cost of maintaining the active state for all participants. The clients with the highest evaluation are elected among the neighbours. In the evaluator, four variables are considered including sample quantity, throughput available, computational capability and the quality of the local dataset. We adopted fuzzy logic as the evaluator since the closed-form solution over four variables does not exist. Extensive simulation results show our proposal approximates the centralized client selection in terms of accuracy and can significantly reduce the communication overhead.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 42 references that are in the map. First posted on arXiv 6 Jan 2024.

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17 of 42 references.

References · 17

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  6. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  7. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  8. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 128
  9. 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 115
  10. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 114
  11. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
  12. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 83
5 more
  1. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  2. 2020An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guaranteecited by 61
  3. 2021Towards Fairness-Aware Federated Learningcited by 57
  4. 2022Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoTcited by 15
  5. 2021Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systemscited by 10

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