mlsanityData to 7 Oct 2026

Interpret Federated Learning with Shapley Values

arXiv 1905.04519 ↗First posted 11 May 2019cs.LG stat.MLcited by 33

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Abstract (arXiv)

Federated Learning is introduced to protect privacy by distributing training data into multiple parties. Each party trains its own model and a meta-model is constructed from the sub models. In this way the details of the data are not disclosed in between each party. In this paper we investigate the model interpretation methods for Federated Learning, specifically on the measurement of feature importance of vertical Federated Learning where feature space of the data is divided into two parties, namely host and guest. For host party to interpret a single prediction of vertical Federated Learning model, the interpretation results, namely the feature importance, are very likely to reveal the protected data from guest party. We propose a method to balance the model interpretability and data privacy in vertical Federated Learning by using Shapley values to reveal detailed feature importance for host features and a unified importance value for federated guest features. Our experiments indicate robust and informative results for interpreting Federated Learning models.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 May 2019.

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Citing papers per year: 2021: 3; 2022: 8; 2023: 11; 2024: 7; 2025: 2; 2026: 2.

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  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  2. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  3. 2022VeriFi: Towards Verifiable Federated Unlearningcited by 41
  4. 2023Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Valuecited by 38
  5. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  6. 2024Advances and Open Challenges in Federated Foundation Modelscited by 33
  7. 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
  8. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  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. 2022Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyondcited by 7
  12. 2023VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarkscited by 6
21 more
  1. 2024A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasurescited by 6
  2. 2024Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Reviewcited by 6
  3. 2021IFedAvg: Interpretable Data-Interoperability for Federated Learningcited by 5
  4. 2023FederatedTrust: A Solution for Trustworthy Federated Learningcited by 5
  5. 2023Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchaincited by 5
  6. 2023Privacy-Preserving Algorithmic Recoursecited by 5
  7. 2024A Survey on Contribution Evaluation in Vertical Federated Learningcited by 5
  8. 2022Hijack Vertical Federated Learning Models As One Partycited by 4
  9. 2023Exploring Data Redundancy in Real-world Image Classification through Data Selectioncited by 3
  10. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  11. 2025Tree-based Models for Vertical Federated Learning: A Surveycited by 3
  12. 2022Local Model Reconstruction Attacks in Federated Learning and their Usescited by 2
  13. 2023Towards Bridging the FL Performance-Explainability Trade-Off: A Trustworthy 6G RAN Slicing Use-Casecited by 2
  14. 2024A Comprehensive Study of Shapley Value in Data Analyticscited by 2
  15. 2023Label Inference Attacks against Node-level Vertical Federated GNNscited by 1
  16. 2022Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restrictioncited by 0
  17. 2022Achieving Transparency in Distributed Machine Learning with Explainable Data Collaborationcited by 0
  18. 2023The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learningcited by 0
  19. 2025Visualizing Machine Learning Models for Enhanced Financial Decision-Making and Risk Managementcited by 0
  20. 2026Federated Rule Ensemble Method in Medical Datacited by 0
  21. 2026FedMTFI: Feature Importance Based Optimized Multi Teacher Knowledge Distillation in Heterogeneous Federated Learning Environmentcited by 0