mlsanityData to 7 Oct 2026

Entity Resolution and Federated Learning get a Federated Resolution

arXiv 1803.04035 ↗First posted 11 Mar 2018cs.DB cs.LGcited by 23

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Abstract (arXiv)

Consider two data providers, each maintaining records of different feature sets about common entities. They aim to learn a linear model over the whole set of features. This problem of federated learning over vertically partitioned data includes a crucial upstream issue: entity resolution, i.e. finding the correspondence between the rows of the datasets. It is well known that entity resolution, just like learning, is mistake-prone in the real world. Despite the importance of the problem, there has been no formal assessment of how errors in entity resolution impact learning. In this paper, we provide a thorough answer to this question, answering how optimal classifiers, empirical losses, margins and generalisation abilities are affected. While our answer spans a wide set of losses --- going beyond proper, convex, or classification calibrated ---, it brings simple practical arguments to upgrade entity resolution as a preprocessing step to learning. One of these suggests that entity resolution should be aimed at controlling or minimizing the number of matching errors between examples of distinct classes. In our experiments, we modify a simple token-based entity resolution algorithm so that it indeed aims at avoiding matching rows belonging to different classes, and perform experiments in the setting where entity resolution relies on noisy data, which is very relevant to real world domains. Notably, our approach covers the case where one peer textitdoes not have classes, or a noisy record of classes. Experiments display that using the class information during entity resolution can buy significant uplift for learning at little expense from the complexity standpoint.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Mar 2018.

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Citing papers per year: 2021: 10; 2022: 5; 2023: 4; 2024: 3; 2025: 1.

23 papers.

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  1. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  2. 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
  3. 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
  4. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  5. 2021A Vertical Federated Learning Framework for Graph Convolutional Networkcited by 20
  6. 2022Federated learning: Applications, challenges and future directionscited by 11
  7. 2021A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learningcited by 10
  8. 2021PIVODL: Privacy-preserving vertical federated learning over distributed labelscited by 9
  9. 2022Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Datacited by 9
  10. 2023Cross-Modal Vertical Federated Learning for MRI Reconstructioncited by 9
  11. 2022ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systemscited by 8
  12. 2022Continual Horizontal Federated Learning for Heterogeneous Datacited by 8
11 more
  1. 2023Federated Learning of Models Pre-Trained on Different Features with Consensus Graphscited by 8
  2. 2023Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Partiescited by 7
  3. 2021Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Ownerscited by 5
  4. 2023A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniquescited by 4
  5. 2021DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topologycited by 3
  6. 2021DVFL: A Vertical Federated Learning Method for Dynamic Datacited by 1
  7. 2024Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Datacited by 1
  8. 2022Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restrictioncited by 0
  9. 2024Survey of Privacy Threats and Countermeasures in Federated Learningcited by 0
  10. 2024Entity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlapcited by 0
  11. 2025Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Datacited by 0