mlsanityData to 14 Oct 2026

Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective

arXiv 2111.11850 ↗First posted 20 Nov 2021cs.GT cs.LGcited by 47

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Abstract (arXiv)

Federated learning (FL) becomes popular and has shown great potentials in training large-scale machine learning (ML) models without exposing the owners' raw data. In FL, the data owners can train ML models based on their local data and only send the model updates rather than raw data to the model owner for aggregation. To improve learning performance in terms of model accuracy and training completion time, it is essential to recruit sufficient participants. Meanwhile, the data owners are rational and may be unwilling to participate in the collaborative learning process due to the resource consumption. To address the issues, there have been various works recently proposed to motivate the data owners to contribute their resources. In this paper, we provide a comprehensive review for the economic and game theoretic approaches proposed in the literature to design various schemes for stimulating data owners to participate in FL training process. In particular, we first present the fundamentals and background of FL, economic theories commonly used in incentive mechanism design. Then, we review applications of game theory and economic approaches applied for incentive mechanisms design of FL. Finally, we highlight some open issues and future research directions concerning incentive mechanism design of FL.

Key facts. Cited by 47 papers in the map (data through 14 Oct 2026). Builds on 50 of its 165 references that are in the map. First posted on arXiv 20 Nov 2021.

Builds on

50 of 165 references.

References · 50

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  3. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 641
  4. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  5. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  6. 2019Towards Efficient Data Valuation Based on the Shapley Valuecited by 281
  7. 2020A Principled Approach to Data Valuation for Federated Learningcited by 127
  8. 2020Collaborative Fairness in Federated Learningcited by 95
  9. 2019Reliable Federated Learning for Mobile Networkscited by 92
  10. 2019Measure Contribution of Participants in Federated Learningcited by 79
  11. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  12. 2020Collaborative Machine Learning with Incentive-Aware Model Rewardscited by 71
38 more
  1. 2020Model-sharing Games: Analyzing Federated Learning Under Voluntary Participationcited by 60
  2. 2020FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MECcited by 48
  3. 2019Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approachcited by 45
  4. 2019Motivating Workers in Federated Learning: A Stackelberg Game Perspectivecited by 45
  5. 2020FedCoin: A Peer-to-Peer Payment System for Federated Learningcited by 39
  6. 2020An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approachcited by 39
  7. 2019Toward an Automated Auction Framework for Wireless Federated Learning Services Marketcited by 38
  8. 2021A Comprehensive Survey of Incentive Mechanism for Federated Learningcited by 38
  9. 2019A Crowdsourcing Framework for On-Device Federated Learningcited by 32
  10. 2020Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approachcited by 29
  11. 2020Hierarchically Fair Federated Learningcited by 28
  12. 2019Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithmcited by 26
  13. 2018Joint Service Pricing and Cooperative Relay Communication for Federated Learningcited by 22
  14. 2020Trading Data For Learning: Incentive Mechanism For On-Device Federated Learningcited by 21
  15. 2020Toward Understanding the Influence of Individual Clients in Federated Learningcited by 16
  16. 2021Transparent Contribution Evaluation for Secure Federated Learning on Blockchaincited by 16
  17. 2020An Exploratory Analysis on Users' Contributions in Federated Learningcited by 15
  18. 2018Towards Secure Blockchain-enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theorycited by 14
  19. 2020Joint Auction-Coalition Formation Framework for Communication-Efficient Federated Learning in UAV-Enabled Internet of Vehiclescited by 14
  20. 2020Estimation of Individual Device Contributions for Incentivizing Federated Learningcited by 14
  21. 2019Collaborative Machine Learning Markets with Data-Replication-Robust Paymentscited by 13
  22. 2021Efficient Client Contribution Evaluation for Horizontal Federated Learningcited by 13
  23. 2021A Contract Theory based Incentive Mechanism for Federated Learningcited by 13
  24. 2020Incentives for Federated Learning: a Hypothesis Elicitation Approachcited by 12
  25. 2020A VCG-based Fair Incentive Mechanism for Federated Learningcited by 11
  26. 2020FedServing: A Federated Prediction Serving Framework Based on Incentive Mechanismcited by 10
  27. 2021Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networkscited by 10
  28. 2021Data-Free Evaluation of User Contributions in Federated Learningcited by 10
  29. 2021Incentive Mechanism Design for Federated Learning: Hedonic Game Approachcited by 8
  30. 2021Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehiclescited by 7
  31. 2021When Crowdsensing Meets Federated Learning: Privacy-Preserving Mobile Crowdsensing Systemcited by 7
  32. 2017Together or Alone: The Price of Privacy in Collaborative Learningcited by 4
  33. 2020Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learningcited by 4
  34. 2020Optimal Procurement Auction for Cooperative Production of Virtual Products: Vickrey-Clarke-Groves Meet Cremer-McLeancited by 2
  35. 2021Applications of Auction and Mechanism Design in Edge Computing: A Surveycited by 2
  36. 2014SACRM: Social Aware Crowdsourcing with Reputation Management in Mobile Sensingcited by 1
  37. 2017Applications of Economic and Pricing Models for Wireless Network Security: A Surveycited by 1
  38. 2021Auction Based Clustered Federated Learning in Mobile Edge Computing Systemcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 14; 2024: 16; 2025: 7; 2026: 5.

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  1. 2024Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Modelscited by 23
  2. 2022Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Reviewcited by 15
  3. 2024Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learningcited by 9
  4. 2021Models of fairness in federated learningcited by 8
  5. 2023Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participationcited by 8
  6. 2026Internet of Agentic AI: Incentive-Compatible Distributed Teaming and Workflowcited by 8
  7. 2022Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networkscited by 6
  8. 2023Strategic Data Sharing between Competitorscited by 6
  9. 2023Incentivizing Honesty among Competitors in Collaborative Learning and Optimizationcited by 6
  10. 2024FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweightingcited by 5
  11. 2023Trustworthy Federated Learning: A Surveycited by 4
  12. 2024Intelligent Agents for Auction-based Federated Learning: A Surveycited by 4
35 more
  1. 2024A Universal Metric of Dataset Similarity for Cross-silo Federated Learningcited by 4
  2. 2024Collaborative Mean Estimation Among Heterogeneous Strategic Agents: Individual Rationality, Fairness, and Truthful Contributioncited by 4
  3. 2023iDML: Incentivized Decentralized Machine Learningcited by 3
  4. 2023Incentivized Communication for Federated Banditscited by 3
  5. 2024FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?cited by 3
  6. 2024Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learningcited by 3
  7. 2024Incentivizing Truthful Collaboration in Heterogeneous Federated Learningcited by 3
  8. 2025Federated Learning from Molecules to Processes: A Perspectivecited by 3
  9. 2021Analysis and Evaluation of Synchronous and Asynchronous FLchaincited by 2
  10. 2023A Game-theoretic Framework for Privacy-preserving Federated Learningcited by 2
  11. 2023A Framework for Incentivized Collaborative Learningcited by 2
  12. 2023Hierarchical Federated Learning Incentivization for Gas Usage Estimationcited by 2
  13. 2023Digital Ethics in Federated Learningcited by 2
  14. 2024An Auction-based Marketplace for Model Trading in Federated Learningcited by 2
  15. 2024Incentivized Truthful Communication for Federated Banditscited by 2
  16. 2024On the Volatility of Shapley-Based Contribution Metrics in Federated Learningcited by 2
  17. 2023Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learningcited by 1
  18. 2023Collaborative Distributed Machine Learningcited by 1
  19. 2025A Novel Collaborative Framework for Efficient Synchronization in Split Federated Learning over Wireless Networkscited by 1
  20. 2025Federated learning, ethics, and the double black box problem in medical AIcited by 1
  21. 2025Incentive-Based Federated Learning: Architectural Elements and Future Directionscited by 1
  22. 2022Distributed Stochastic Gradient Descent with Cost-Sensitive and Strategic Agentscited by 0
  23. 2023Cross-Fusion Rule for Personalized Federated Learningcited by 0
  24. 2023Towards Realistic Mechanisms That Incentivize Federated Participation and Contributioncited by 0
  25. 2024PPBFL: A Privacy Protected Blockchain-based Federated Learning Modelcited by 0
  26. 2024P3LS: Partial Least Squares under Privacy Preservationcited by 0
  27. 2024A Bargaining-based Approach for Feature Trading in Vertical Federated Learningcited by 0
  28. 2024Privacy-Preserving and Trustworthy Deep Learning for Medical Imagingcited by 0
  29. 2025Energy Minimization for Participatory Federated Learning in IoT Analyzed via Game Theorycited by 0
  30. 2025CARE: Compatibility-Aware Incentive Mechanisms for Federated Learning with Budgeted Requesterscited by 0
  31. 2025Long-Term Client Selection for Federated Learning with Non-IID Data: A Truthful Auction Approachcited by 0
  32. 2026Mechanism Design for Federated Learning with Non-Monotonic Network Effectscited by 0
  33. 2026Knowledge-Free Correlated Agreement for Incentivizing Federated Learningcited by 0
  34. 2026Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approachcited by 0
  35. 2026Reputation-driven Cooperation in Lattice-based Decentralized Federated Learning through Evolutionary Game Theorycited by 0