mlsanityData to 14 Oct 2026

Measure Contribution of Participants in Federated Learning

arXiv 1909.08525 ↗First posted 17 Sep 2019cs.LG stat.MLcited by 79

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Abstract (arXiv)

Federated Machine Learning (FML) creates an ecosystem for multiple parties to collaborate on building models while protecting data privacy for the participants. A measure of the contribution for each party in FML enables fair credits allocation. In this paper we develop simple but powerful techniques to fairly calculate the contributions of multiple parties in FML, in the context of both horizontal FML and vertical FML. For Horizontal FML we use deletion method to calculate the grouped instance influence. For Vertical FML we use Shapley Values to calculate the grouped feature importance. Our methods open the door for research in model contribution and credit allocation in the context of federated machine learning.

Key facts. Cited by 79 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Sep 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 12; 2022: 8; 2023: 19; 2024: 23; 2025: 10; 2026: 7.

79 papers.

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  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  2. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 99
  3. 2023Machine Unlearning: Solutions and Challengescited by 93
  4. 2021Federated Reinforcement Learning: Techniques, Applications, and Open Challengescited by 81
  5. 2021GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learningcited by 74
  6. 2021Towards Fairness-Aware Federated Learningcited by 58
  7. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  8. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 41
  9. 2021A Comprehensive Survey of Incentive Mechanism for Federated Learningcited by 38
  10. 2024Advances and Open Challenges in Federated Foundation Modelscited by 36
  11. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  12. 2024Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Modelscited by 23
38 more
  1. 2021Improving Fairness for Data Valuation in Horizontal Federated Learningcited by 22
  2. 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
  3. 2022Fair and efficient contribution valuation for vertical federated learningcited by 18
  4. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  5. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  6. 2021FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learningcited by 14
  7. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  8. 2021Efficient Client Contribution Evaluation for Horizontal Federated Learningcited by 13
  9. 2023FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Schedulercited by 13
  10. 2023Towards Interpretable Federated Learningcited by 11
  11. 2021Data-Free Evaluation of User Contributions in Federated Learningcited by 10
  12. 2022A Snapshot of the Frontiers of Client Selection in Federated Learningcited by 9
  13. 2025Stochastic Self-Organization in Multi-Agent Systemscited by 9
  14. 2021Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learningcited by 7
  15. 2023Towards Algorithmic Fairness by means of Instance-level Data Re-weighting based on Shapley Valuescited by 7
  16. 2023A Survey of Federated Evaluation in Federated Learningcited by 7
  17. 2023Greedy Shapley Client Selection for Communication-Efficient Federated Learningcited by 7
  18. 2024Influence-Oriented Personalized Federated Learningcited by 7
  19. 2023VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarkscited by 6
  20. 2024Vertical Federated Unlearning via Backdoor Certificationcited by 6
  21. 2022Performance Weighting for Robust Federated Learning Against Corrupted Sourcescited by 5
  22. 2022Incentivizing Federated Learningcited by 5
  23. 2023Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchaincited by 5
  24. 2024A Survey on Contribution Evaluation in Vertical Federated Learningcited by 5
  25. 2024ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learningcited by 5
  26. 2022Cerberus: Exploring Federated Prediction of Security Eventscited by 4
  27. 2023Entropy-driven Fair and Effective Federated Learningcited by 4
  28. 2023iDML: Incentivized Decentralized Machine Learningcited by 3
  29. 2023Value of Information and Timing-aware Scheduling for Federated Learningcited by 3
  30. 2024Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participationcited by 3
  31. 2024Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectivescited by 3
  32. 2025Adaptive Composition of Machine Learning as a Service (MLaaS) for IoT Environmentscited by 3
  33. 2021Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approachcited by 2
  34. 2023Hierarchical Federated Learning Incentivization for Gas Usage Estimationcited by 2
  35. 2023Incentive Allocation in Vertical Federated Learning Based on Bankruptcy Problemcited by 2
  36. 2023Proof-of-Federated-Learning-Subchain: Free Partner Selection Subchain Based on Federated Learningcited by 2
  37. 2024Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learningcited by 2
  38. 2024On the Volatility of Shapley-Based Contribution Metrics in Federated Learningcited by 2

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