mlsanityData to 14 Oct 2026

Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption

arXiv 1711.10677 ↗First posted 29 Nov 2017cs.LGcited by 176

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Abstract (arXiv)

Consider two data providers, each maintaining private records of different feature sets about common entities. They aim to learn a linear model jointly in a federated setting, namely, data is local and a shared model is trained from locally computed updates. In contrast with most work on distributed learning, in this scenario (i) data is split vertically, i.e. by features, (ii) only one data provider knows the target variable and (iii) entities are not linked across the data providers. Hence, to the challenge of private learning, we add the potentially negative consequences of mistakes in entity resolution. Our contribution is twofold. First, we describe a three-party end-to-end solution in two phases ---privacy-preserving entity resolution and federated logistic regression over messages encrypted with an additively homomorphic scheme---, secure against a honest-but-curious adversary. The system allows learning without either exposing data in the clear or sharing which entities the data providers have in common. Our implementation is as accurate as a naive non-private solution that brings all data in one place, and scales to problems with millions of entities with hundreds of features. Second, we provide what is to our knowledge the first formal analysis of the impact of entity resolution's mistakes on learning, with results on how optimal classifiers, empirical losses, margins and generalisation abilities are affected. Our results bring a clear and strong support for federated learning: under reasonable assumptions on the number and magnitude of entity resolution's mistakes, it can be extremely beneficial to carry out federated learning in the setting where each peer's data provides a significant uplift to the other.

Key facts. Cited by 176 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Nov 2017.

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Citing papers per year: 2021: 47; 2022: 44; 2023: 37; 2024: 27; 2025: 14; 2026: 7.

176 papers.

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  1. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  2. 2021A Field Guide to Federated Optimizationcited by 315
  3. 2021Trustworthy AI: From Principles to Practicescited by 207
  4. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  5. 2022Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakagecited by 72
  6. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 66
  7. 2023FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Modelscited by 63
  8. 2021Privacy-Preserving Machine Learning: Methods, Challenges and Directionscited by 58
  9. 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
  10. 2021Survey: Leakage and Privacy at Inference Timecited by 44
  11. 2022A Survey on Heterogeneous Federated Learningcited by 42
  12. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 41
38 more
  1. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 39
  2. 2021Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updatingcited by 37
  3. 2021GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learningcited by 36
  4. 2022BlindFL: Vertical Federated Machine Learning without Peeking into Your Datacited by 36
  5. 2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
  6. 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
  7. 2021Federated Graph Learning -- A Position Papercited by 35
  8. 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
  9. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  10. 2021Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretabilitycited by 28
  11. 2023A Survey on Decentralized Federated Learningcited by 27
  12. 2021Personalized Federated Learning with Adaptive Batchnorm for Healthcarecited by 26
  13. 2022Differentially Private Label Protection in Split Learningcited by 26
  14. 2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
  15. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  16. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  17. 2021A Vertical Federated Learning Framework for Graph Convolutional Networkcited by 21
  18. 2021Memory-aware curriculum federated learning for breast cancer classificationcited by 20
  19. 2021Robust Federated Learning with Attack-Adaptive Aggregationcited by 17
  20. 2022A Hybrid Self-Supervised Learning Framework for Vertical Federated Learningcited by 17
  21. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  22. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  23. 2021AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilizationcited by 16
  24. 2023A Survey for Federated Learning Evaluations: Goals and Measurescited by 16
  25. 2022AGIC: Approximate Gradient Inversion Attack on Federated Learningcited by 15
  26. 2023FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learningcited by 15
  27. 2023FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attackscited by 15
  28. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  29. 2022Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clusteringcited by 14
  30. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  31. 2022Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated Learningcited by 13
  32. 2023A Survey on Class Imbalance in Federated Learningcited by 13
  33. 2023Communication-Efficient Vertical Federated Learning with Limited Overlapping Samplescited by 13
  34. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  35. 2022Federated learning: Applications, challenges and future directionscited by 11
  36. 2023Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learningcited by 11
  37. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  38. 2021A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learningcited by 10

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