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FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC

arXiv 2002.09699 ↗First posted 22 Feb 2020cs.LG cs.GT stat.MLcited by 48

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Abstract (arXiv)

Promising federated learning coupled with Mobile Edge Computing (MEC) is considered as one of the most promising solutions to the AI-driven service provision. Plenty of studies focus on federated learning from the performance and security aspects, but they neglect the incentive mechanism. In MEC, edge nodes would not like to voluntarily participate in learning, and they differ in the provision of multi-dimensional resources, both of which might deteriorate the performance of federated learning. Also, lightweight schemes appeal to edge nodes in MEC. These features require the incentive mechanism to be well designed for MEC. In this paper, we present an incentive mechanism FMore with multi-dimensional procurement auction of K winners. Our proposal FMore not only is lightweight and incentive compatible, but also encourages more high-quality edge nodes with low cost to participate in learning and eventually improve the performance of federated learning. We also present theoretical results of Nash equilibrium strategy to edge nodes and employ the expected utility theory to provide guidance to the aggregator. Both extensive simulations and real-world experiments demonstrate that the proposed scheme can effectively reduce the training rounds and drastically improve the model accuracy for challenging AI tasks.

Key facts. Cited by 48 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Feb 2020.

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Citing papers per year: 2021: 11; 2022: 6; 2023: 17; 2024: 9; 2025: 4; 2026: 1.

48 papers.

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  1. 2021GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learningcited by 74
  2. 2021Towards Fairness-Aware Federated Learningcited by 58
  3. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  4. 2021A Comprehensive Survey of Incentive Mechanism for Federated Learningcited by 38
  5. 2024Advances and Open Challenges in Federated Foundation Modelscited by 36
  6. 2023Federated Domain Generalization: A Surveycited by 25
  7. 2023FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUscited by 20
  8. 2021Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspectivecited by 14
  9. 2021Faithful Edge Federated Learning: Scalability and Privacycited by 13
  10. 2021MarS-FL: Enabling Competitors to Collaborate in Federated Learningcited by 11
  11. 2021Data-Free Evaluation of User Contributions in Federated Learningcited by 10
  12. 2023Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Modelscited by 10
36 more
  1. 2022FedToken: Tokenized Incentives for Data Contribution in Federated Learningcited by 9
  2. 2022A Snapshot of the Frontiers of Client Selection in Federated Learningcited by 9
  3. 2023Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participationcited by 8
  4. 2023Utility-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learningcited by 7
  5. 2022Online Auction-Based Incentive Mechanism Design for Horizontal Federated Learning with Budget Constraintcited by 6
  6. 2023FedDebug: Systematic Debugging for Federated Learning Applicationscited by 6
  7. 2022Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurementcited by 5
  8. 2022Incentivizing Federated Learningcited by 5
  9. 2022Joint Optimization of Energy Consumption and Completion Time in Federated Learningcited by 5
  10. 2023BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learningcited by 5
  11. 2023On the Effect of Defections in Federated Learning and How to Prevent Themcited by 5
  12. 2024ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learningcited by 5
  13. 2021Federated Learning: Issues in Medical Applicationcited by 4
  14. 2024Intelligent Agents for Auction-based Federated Learning: A Surveycited by 4
  15. 2023Exploring Data Redundancy in Real-world Image Classification through Data Selectioncited by 3
  16. 2023Multi-Session Budget Optimization for Forward Auction-based Federated Learningcited by 3
  17. 2024IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Contentcited by 3
  18. 2021Reward-Based 1-bit Compressed Federated Distillation on Blockchaincited by 2
  19. 2023A Framework for Incentivized Collaborative Learningcited by 2
  20. 2023Hierarchical Federated Learning Incentivization for Gas Usage Estimationcited by 2
  21. 2023Incentive Allocation in Vertical Federated Learning Based on Bankruptcy Problemcited by 2
  22. 2021Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A Stackelberg Game Approachcited by 1
  23. 2023Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspectivecited by 1
  24. 2023Federated Learning Incentive Mechanism under Buyers' Auction Marketcited by 1
  25. 2023Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learningcited by 1
  26. 2024Federated Learning for Data Market: Shapley-UCB for Seller Selection and Incentivescited by 1
  27. 2024Acoustic Model Optimization over Multiple Data Sources: Merging and Valuationcited by 1
  28. 2024FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learningcited by 1
  29. 2025FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selectioncited by 1
  30. 2023Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learningcited by 0
  31. 2024Agent-oriented Joint Decision Support for Data Owners in Auction-based Federated Learningcited by 0
  32. 2024FedCross: Intertemporal Federated Learning Under Evolutionary Gamescited by 0
  33. 2025CARE: Compatibility-Aware Incentive Mechanisms for Federated Learning with Budgeted Requesterscited by 0
  34. 2025WallStreetFeds: Client-Specific Tokens as Investment Vehicles in Federated Learningcited by 0
  35. 2025Strategic Incentivization for Locally Differentially Private Federated Learningcited by 0
  36. 2026Knowledge-Free Correlated Agreement for Incentivizing Federated Learningcited by 0