mlsanityData to 7 Oct 2026

FedCoin: A Peer-to-Peer Payment System for Federated Learning

arXiv 2002.11711 ↗First posted 26 Feb 2020cs.CR cs.LG stat.MLcited by 39

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Abstract (arXiv)

Federated learning (FL) is an emerging collaborative machine learning method to train models on distributed datasets with privacy concerns. To properly incentivize data owners to contribute their efforts, Shapley Value (SV) is often adopted to fairly assess their contribution. However, the calculation of SV is time-consuming and computationally costly. In this paper, we propose FedCoin, a blockchain-based peer-to-peer payment system for FL to enable a feasible SV based profit distribution. In FedCoin, blockchain consensus entities calculate SVs and a new block is created based on the proof of Shapley (PoSap) protocol. It is in contrast to the popular BitCoin network where consensus entities "mine" new blocks by solving meaningless puzzles. Based on the computed SVs, a scheme for dividing the incentive payoffs among FL clients with nonrepudiation and tamper-resistance properties is proposed. Experimental results based on real-world data show that FedCoin can promote high-quality data from FL clients through accurately computing SVs with an upper bound on the computational resources required for reaching consensus. It opens opportunities for non-data owners to play a role in FL.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Feb 2020.

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Citing papers per year: 2021: 12; 2022: 6; 2023: 8; 2024: 6; 2025: 6; 2026: 1.

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  1. 2021Federated Quantum Machine Learningcited by 82
  2. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  3. 2021A Comprehensive Survey of Incentive Mechanism for Federated Learningcited by 38
  4. 2024Advances and Open Challenges in Federated Foundation Modelscited by 33
  5. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  6. 2023A Survey on Decentralized Federated Learningcited by 27
  7. 2023Federated Generative Learning with Foundation Modelscited by 23
  8. 2021Architectural Patterns for the Design of Federated Learning Systemscited by 17
  9. 2021A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guaranteecited by 16
  10. 2021A Systematic Survey of Blockchained Federated Learningcited by 15
  11. 2022Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Reviewcited by 15
  12. 2022Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learningcited by 13
27 more
  1. 2021Efficient Client Contribution Evaluation for Horizontal Federated Learningcited by 12
  2. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  3. 2021Data-Free Evaluation of User Contributions in Federated Learningcited by 10
  4. 2021OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learningcited by 9
  5. 2022A Snapshot of the Frontiers of Client Selection in Federated Learningcited by 9
  6. 2024Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learningcited by 8
  7. 2025dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysiscited by 8
  8. 2021Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learningcited by 7
  9. 2022Reward Systems for Trustworthy Medical Federated Learningcited by 6
  10. 2022Incentivizing Federated Learningcited by 5
  11. 2023Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchaincited by 5
  12. 2023Contribution Evaluation in Federated Learning: Examining Current Approachescited by 3
  13. 2021Reward-Based 1-bit Compressed Federated Distillation on Blockchaincited by 2
  14. 2024An Auction-based Marketplace for Model Trading in Federated Learningcited by 2
  15. 2024A Comprehensive Study of Shapley Value in Data Analyticscited by 2
  16. 2022Federated Named Entity Recognitioncited by 1
  17. 2023VeryFL: A Verify Federated Learning Framework Embedded with Blockchaincited by 1
  18. 2024FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learningcited by 1
  19. 2025Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Ownerscited by 1
  20. 2025A Bayesian Incentive Mechanism for Poison-Resilient Federated Learningcited by 1
  21. 2025Blockchain-Enabled Federated Learningcited by 1
  22. 2023Scalable Data Point Valuation in Decentralized Learningcited by 0
  23. 2023A Blockchain-based Platform for Reliable Inference and Training of Large-Scale Modelscited by 0
  24. 2024DPVS-Shapley:Faster and Universal Contribution Evaluation Component in Federated Learningcited by 0
  25. 2025HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrastcited by 0
  26. 2025Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor Itcited by 0
  27. 2026A Lifecycle Cost Analysis of Smart-Contract-Coordinated Federated Learning Marketplacescited by 0