mlsanityData to 14 Oct 2026

A Comprehensive Survey of Incentive Mechanism for Federated Learning

arXiv 2106.15406 ↗First posted 27 Jun 2021cs.LG cs.GTcited by 38

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Abstract (arXiv)

Federated learning utilizes various resources provided by participants to collaboratively train a global model, which potentially address the data privacy issue of machine learning. In such promising paradigm, the performance will be deteriorated without sufficient training data and other resources in the learning process. Thus, it is quite crucial to inspire more participants to contribute their valuable resources with some payments for federated learning. In this paper, we present a comprehensive survey of incentive schemes for federate learning. Specifically, we identify the incentive problem in federated learning and then provide a taxonomy for various schemes. Subsequently, we summarize the existing incentive mechanisms in terms of the main techniques, such as Stackelberg game, auction, contract theory, Shapley value, reinforcement learning, blockchain. By reviewing and comparing some impressive results, we figure out three directions for the future study.

Key facts. Cited by 38 papers in the map (data through 14 Oct 2026). Builds on 27 of its 58 references that are in the map. First posted on arXiv 27 Jun 2021.

Builds on

27 of 58 references.

References · 27

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 629
  4. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  5. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  6. 2019Federated Machine Learning: Concept and Applicationscited by 412
  7. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  8. 2019Towards Efficient Data Valuation Based on the Shapley Valuecited by 281
  9. 2018Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learningcited by 246
  10. 2019Measure Contribution of Participants in Federated Learningcited by 79
  11. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  12. 2020FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MECcited by 48
15 more
  1. 2019Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approachcited by 45
  2. 2019Motivating Workers in Federated Learning: A Stackelberg Game Perspectivecited by 45
  3. 2020FedCoin: A Peer-to-Peer Payment System for Federated Learningcited by 39
  4. 2019Toward an Automated Auction Framework for Wireless Federated Learning Services Marketcited by 38
  5. 2020Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approachcited by 29
  6. 2018Joint Service Pricing and Cooperative Relay Communication for Federated Learningcited by 22
  7. 2020Trading Data For Learning: Incentive Mechanism For On-Device Federated Learningcited by 21
  8. 2020Estimation of Individual Device Contributions for Incentivizing Federated Learningcited by 14
  9. 2020Incentives for Federated Learning: a Hypothesis Elicitation Approachcited by 12
  10. 2020A VCG-based Fair Incentive Mechanism for Federated Learningcited by 11
  11. 2020FedServing: A Federated Prediction Serving Framework Based on Incentive Mechanismcited by 10
  12. 2019From Server-Based to Client-Based Machine Learning: A Comprehensive Surveycited by 7
  13. 2020Mechanism Design for Multi-Party Machine Learningcited by 6
  14. 20202CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environmentscited by 6
  15. 2020Incentive Mechanism Design for Distributed Coded Machine Learningcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 6; 2023: 14; 2024: 8; 2025: 3; 2026: 5.

38 papers.

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  1. 2021Towards Fairness-Aware Federated Learningcited by 58
  2. 2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimizationcited by 56
  3. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  4. 2023FS-Real: Towards Real-World Cross-Device Federated Learningcited by 22
  5. 2023Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Studycited by 21
  6. 2023FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUscited by 20
  7. 2022Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Reviewcited by 15
  8. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 15
  9. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  10. 2022ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systemscited by 9
  11. 2022A Snapshot of the Frontiers of Client Selection in Federated Learningcited by 9
  12. 2024Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learningcited by 9
26 more
  1. 2023Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participationcited by 8
  2. 2023FedDebug: Systematic Debugging for Federated Learning Applicationscited by 6
  3. 2023Strategic Data Sharing between Competitorscited by 6
  4. 2024FedFair^3: Unlocking Threefold Fairness in Federated Learningcited by 5
  5. 2023Trustworthy Federated Learning: A Surveycited by 4
  6. 2024Intelligent Agents for Auction-based Federated Learning: A Surveycited by 4
  7. 2023A Framework for Incentivized Collaborative Learningcited by 2
  8. 2023Hierarchical Federated Learning Incentivization for Gas Usage Estimationcited by 2
  9. 2024Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approachcited by 2
  10. 2024On the Volatility of Shapley-Based Contribution Metrics in Federated Learningcited by 2
  11. 2024Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchaincited by 2
  12. 2025DICE: Data Influence Cascade in Decentralized Learningcited by 2
  13. 2023Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspectivecited by 1
  14. 2023Federated Learning Incentive Mechanism under Buyers' Auction Marketcited by 1
  15. 2024Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Reviewcited by 1
  16. 2025Incentivizing Inclusive Contributions in Model Sharing Marketscited by 1
  17. 2022Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restrictioncited by 0
  18. 2022TorchFL: A Performant Library for Bootstrapping Federated Learning Experimentscited by 0
  19. 2023Towards Realistic Mechanisms That Incentivize Federated Participation and Contributioncited by 0
  20. 2023AIJack: Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learningcited by 0
  21. 2025Learning with Limited Shared Information in Multi-agent Multi-armed Banditcited by 0
  22. 2026Mechanism Design for Federated Learning with Non-Monotonic Network Effectscited by 0
  23. 2026Knowledge-Free Correlated Agreement for Incentivizing Federated Learningcited by 0
  24. 2026Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participationcited by 0
  25. 2026Shapley-Value-Based Feature Attribution for Data Maskingcited by 0
  26. 2026Reputation-driven Cooperation in Lattice-based Decentralized Federated Learning through Evolutionary Game Theorycited by 0