mlsanityData to 7 Oct 2026

Mechanisms that Incentivize Data Sharing in Federated Learning

arXiv 2207.04557 ↗First posted 10 Jul 2022cs.GT cs.CY cs.DCcited by 38

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Abstract (arXiv)

Federated learning is typically considered a beneficial technology which allows multiple agents to collaborate with each other, improve the accuracy of their models, and solve problems which are otherwise too data-intensive / expensive to be solved individually. However, under the expectation that other agents will share their data, rational agents may be tempted to engage in detrimental behavior such as free-riding where they contribute no data but still enjoy an improved model. In this work, we propose a framework to analyze the behavior of such rational data generators. We first show how a naive scheme leads to catastrophic levels of free-riding where the benefits of data sharing are completely eroded. Then, using ideas from contract theory, we introduce accuracy shaping based mechanisms to maximize the amount of data generated by each agent. These provably prevent free-riding without needing any payment mechanism.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). Builds on 26 of its 57 references that are in the map. First posted on arXiv 10 Jul 2022.

Builds on

26 of 57 references.

References · 26

  1. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2020Scaling Laws for Neural Language Modelscited by 6,411
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  6. 2019Towards Federated Learning at Scale: System Designcited by 826
  7. 2020The Future of Digital Health with Federated Learningcited by 625
  8. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  9. 2019Agnostic Federated Learningcited by 451
  10. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  11. 2019Towards Efficient Data Valuation Based on the Shapley Valuecited by 279
  12. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 148
14 more
  1. 2020A Principled Approach to Data Valuation for Federated Learningcited by 127
  2. 2018Blockchained On-Device Federated Learningcited by 105
  3. 2020Collaborative Machine Learning with Incentive-Aware Model Rewardscited by 71
  4. 2020Model-sharing Games: Analyzing Federated Learning Under Voluntary Participationcited by 60
  5. 2020Free-rider Attacks on Model Aggregation in Federated Learningcited by 57
  6. 2019Free-riders in Federated Learning: Attacks and Defensescited by 48
  7. 2019Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approachcited by 45
  8. 2019Motivating Workers in Federated Learning: A Stackelberg Game Perspectivecited by 45
  9. 2021Optimality and Stability in Federated Learning: A Game-theoretic Approachcited by 40
  10. 2020Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approachcited by 29
  11. 2020An Exploratory Analysis on Users' Contributions in Federated Learningcited by 15
  12. 2018Towards Secure Blockchain-enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theorycited by 14
  13. 2021Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspectivecited by 14
  14. 2021A Contract Theory based Incentive Mechanism for Federated Learningcited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 12; 2024: 10; 2025: 11; 2026: 5.

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  1. 2023Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshnesscited by 15
  2. 2024Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learningcited by 9
  3. 2024Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learningcited by 9
  4. 2023Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learningcited by 8
  5. 2023Strategic Data Sharing between Competitorscited by 6
  6. 2023Incentivizing Honesty among Competitors in Collaborative Learning and Optimizationcited by 6
  7. 2023Scaff-PD: Communication Efficient Fair and Robust Federated Learningcited by 6
  8. 2023The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learningcited by 5
  9. 2025Multiplayer Federated Learning: Reaching Equilibrium with Less Communicationcited by 5
  10. 2023On the Effect of Defections in Federated Learning and How to Prevent Themcited by 4
  11. 2024Collaborative Mean Estimation Among Heterogeneous Strategic Agents: Individual Rationality, Fairness, and Truthful Contributioncited by 4
  12. 2023Incentivized Communication for Federated Banditscited by 3
26 more
  1. 2024FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?cited by 3
  2. 2024Incentivizing Truthful Collaboration in Heterogeneous Federated Learningcited by 3
  3. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  4. 2024An Auction-based Marketplace for Model Trading in Federated Learningcited by 2
  5. 2024Incentivized Truthful Communication for Federated Banditscited by 2
  6. 2025Adaptive collaboration for online personalized distributed learning with heterogeneous clientscited by 2
  7. 2023Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learningcited by 1
  8. 2025Incentivizing Inclusive Contributions in Model Sharing Marketscited by 1
  9. 2025Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Ownerscited by 1
  10. 2023No Bidding, No Regret: Pairwise-Feedback Mechanisms for Digital Goods and Data Auctionscited by 0
  11. 2023Tackling Computational Heterogeneity in FL: A Few Theoretical Insightscited by 0
  12. 2023Towards Realistic Mechanisms That Incentivize Federated Participation and Contributioncited by 0
  13. 2024Defection-Free Collaboration between Competitors in a Learning Systemcited by 0
  14. 2024Mechanisms for Data Sharing in Collaborative Causal Inference (Extended Version)cited by 0
  15. 2024Paid with Models: Optimal Contract Design for Collaborative Machine Learningcited by 0
  16. 2025CoRAG: Collaborative Retrieval-Augmented Generationcited by 0
  17. 2025A Cram'er-von Mises Approach to Incentivizing Truthful Data Sharingcited by 0
  18. 2025Incentivizing High-quality Participation From Federated Learning Agentscited by 0
  19. 2025A Mechanism for Mutual Fairness in Cooperative Games with Replicable Resources -- Extended Versioncited by 0
  20. 2025Incentivizing Time-Aware Fairness in Data Sharingcited by 0
  21. 2025MoB: Mixture of Bidderscited by 0
  22. 2026Mechanism Design for Federated Learning with Non-Monotonic Network Effectscited by 0
  23. 2026Incentive-Aware Federated Averaging with Performance Guarantees under Strategic Participationcited by 0
  24. 2026Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participationcited by 0
  25. 2026Performative Privacy: When Differential Privacy Maximizes Utilitycited by 0
  26. 2026Pathwise Individual Rationality in Federated Learning: A Mechanism-Architecture Co-Designcited by 0