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Motivating Workers in Federated Learning: A Stackelberg Game Perspective

arXiv 1908.03092 ↗First posted 6 Aug 2019cs.DCcited by 45

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Abstract (arXiv)

Due to the large size of the training data, distributed learning approaches such as federated learning have gained attention recently. However, the convergence rate of distributed learning suffers from heterogeneous worker performance. In this paper, we consider an incentive mechanism for workers to mitigate the delays in completion of each batch. We analytically obtained equilibrium solution of a Stackelberg game. Our numerical results indicate that with a limited budget, the model owner should judiciously decide on the number of workers due to trade off between the diversity provided by the number of workers and the latency of completing the training.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Aug 2019.

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Citing papers per year: 2021: 14; 2022: 11; 2023: 7; 2024: 4; 2025: 6; 2026: 3.

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  1. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  2. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  3. 2021Towards Fairness-Aware Federated Learningcited by 57
  4. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  5. 2021A Comprehensive Survey of Incentive Mechanism for Federated Learningcited by 38
  6. 2022Cross-Silo Federated Learning: Challenges and Opportunitiescited by 38
  7. 2022Mechanisms that Incentivize Data Sharing in Federated Learningcited by 38
  8. 2024Advances and Open Challenges in Federated Foundation Modelscited by 33
  9. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
  10. 2025Ten Challenging Problems in Federated Foundation Modelscited by 20
  11. 2022Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Designcited by 15
  12. 2021Faithful Edge Federated Learning: Scalability and Privacycited by 13
33 more
  1. 2021A Contract Theory based Incentive Mechanism for Federated Learningcited by 13
  2. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  3. 2021MarS-FL: Enabling Competitors to Collaborate in Federated Learningcited by 11
  4. 2021Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networkscited by 10
  5. 2021FL-Market: Trading Private Models in Federated Learningcited by 9
  6. 2022A Snapshot of the Frontiers of Client Selection in Federated Learningcited by 9
  7. 2022Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspectivecited by 8
  8. 2023Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participationcited by 8
  9. 2023Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learningcited by 8
  10. 2021Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehiclescited by 7
  11. 2022Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learningcited by 6
  12. 2023FedDebug: Systematic Debugging for Federated Learning Applicationscited by 6
  13. 2022Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurementcited by 5
  14. 2022Incentivizing Federated Learningcited by 5
  15. 2021Federated Learning: Issues in Medical Applicationcited by 4
  16. 2021Energy Efficient Federated Learning in Integrated Fog-Cloud Computing Enabled Internet-of-Things Networkscited by 3
  17. 2022Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgetscited by 2
  18. 2023Incentive Allocation in Vertical Federated Learning Based on Bankruptcy Problemcited by 2
  19. 2024On the Volatility of Shapley-Based Contribution Metrics in Federated Learningcited by 2
  20. 2021Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A Stackelberg Game Approachcited by 1
  21. 2023Price-Discrimination Game for Distributed Resource Management in Federated Learningcited by 1
  22. 2023Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learningcited by 1
  23. 2024Federated Learning for Data Market: Shapley-UCB for Seller Selection and Incentivescited by 1
  24. 2024Acoustic Model Optimization over Multiple Data Sources: Merging and Valuationcited by 1
  25. 2025Carpe Diem: Critical Learning Period-Aware Contract-Based Incentives for Federated Learningcited by 1
  26. 2022Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restrictioncited by 0
  27. 2025Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selectioncited by 0
  28. 2025Strategic Incentivization for Locally Differentially Private Federated Learningcited by 0
  29. 2025Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor Itcited by 0
  30. 2025CoSIFL: Collaborative Secure and Incentivized Federated Learning with Differential Privacycited by 0
  31. 2026Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participationcited by 0
  32. 2026Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approachcited by 0
  33. 2026A Game-Theoretic Framework for Incentive-Compatible AI training Under Renewable-Energy Constraintscited by 0