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Long-Term Client Selection for Federated Learning with Non-IID Data: A Truthful Auction Approach

arXiv 2508.09181 ↗First posted 7 Aug 2025cs.LG cs.AI cs.SYcited by 0

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Abstract (arXiv)

Federated learning (FL) provides a decentralized framework that enables universal model training through collaborative efforts on mobile nodes, such as smart vehicles in the Internet of Vehicles (IoV). Each smart vehicle acts as a mobile client, contributing to the process without uploading local data. This method leverages non-independent and identically distributed (non-IID) training data from different vehicles, influenced by various driving patterns and environmental conditions, which can significantly impact model convergence and accuracy. Although client selection can be a feasible solution for non-IID issues, it faces challenges related to selection metrics. Traditional metrics evaluate client data quality independently per round and require client selection after all clients complete local training, leading to resource wastage from unused training results. In the IoV context, where vehicles have limited connectivity and computational resources, information asymmetry in client selection risks clients submitting false information, potentially making the selection ineffective. To tackle these challenges, we propose a novel Long-term Client-Selection Federated Learning based on Truthful Auction (LCSFLA). This scheme maximizes social welfare with consideration of long-term data quality using a new assessment mechanism and energy costs, and the advised auction mechanism with a deposit requirement incentivizes client participation and ensures information truthfulness. We theoretically prove the incentive compatibility and individual rationality of the advised incentive mechanism. Experimental results on various datasets, including those from IoV scenarios, demonstrate its effectiveness in mitigating performance degradation caused by non-IID data.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 49 references that are in the map. First posted on arXiv 7 Aug 2025.

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References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2018Federated Learning with Non-IID Datacited by 1,108
  4. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  5. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  6. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
  7. 2020A Principled Approach to Data Valuation for Federated Learningcited by 127
  8. 2018In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learningcited by 80
  9. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  10. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  11. 2019Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approachcited by 45
  12. 2018Multi-objective Evolutionary Federated Learningcited by 42
5 more
  1. 2020Stochastic Client Selection for Federated Learning with Volatile Clientscited by 39
  2. 2019Toward an Automated Auction Framework for Wireless Federated Learning Services Marketcited by 38
  3. 2021Client Selection in Federated Learning based on Gradients Importancecited by 9
  4. 2024Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoVcited by 7
  5. 2023DPP-based Client Selection for Federated Learning with Non-IID Datacited by 3

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