# Entity Resolution and Federated Learning get a Federated Resolution

arXiv: https://arxiv.org/abs/1803.04035 · HTML: https://mlsanity.com/paper/1803.04035/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2018.

## Built on by (most cited first, 23 of 23)

- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN](https://mlsanity.com/paper/2104.00489/) (2021)
- [FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data](https://mlsanity.com/paper/2103.03918/) (2021)
- [The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning](https://mlsanity.com/paper/2111.05326/) (2021)
- [A Vertical Federated Learning Framework for Graph Convolutional Network](https://mlsanity.com/paper/2106.11593/) (2021)
- [Federated learning: Applications, challenges and future directions](https://mlsanity.com/paper/2205.09513/) (2022)
- [A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning](https://mlsanity.com/paper/2106.06312/) (2021)
- [PIVODL: Privacy-preserving vertical federated learning over distributed labels](https://mlsanity.com/paper/2108.11444/) (2021)
- [ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems](https://mlsanity.com/paper/2201.02775/) (2022)
- [Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data](https://mlsanity.com/paper/2203.01752/) (2022)
- [Cross-Modal Vertical Federated Learning for MRI Reconstruction](https://mlsanity.com/paper/2306.02673/) (2023)
- [Continual Horizontal Federated Learning for Heterogeneous Data](https://mlsanity.com/paper/2203.02108/) (2022)
- [Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs](https://mlsanity.com/paper/2306.01240/) (2023)
- [Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties](https://mlsanity.com/paper/2303.18178/) (2023)
- [Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners](https://mlsanity.com/paper/2106.05468/) (2021)
- [A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques](https://mlsanity.com/paper/2302.08571/) (2023)
- [DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topology](https://mlsanity.com/paper/2111.05505/) (2021)
- [DVFL: A Vertical Federated Learning Method for Dynamic Data](https://mlsanity.com/paper/2111.03341/) (2021)
- [Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data](https://mlsanity.com/paper/2403.03871/) (2024)
- [Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction](https://mlsanity.com/paper/2202.05139/) (2022)
- [Survey of Privacy Threats and Countermeasures in Federated Learning](https://mlsanity.com/paper/2402.00342/) (2024)
- [Entity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlap](https://mlsanity.com/paper/2406.17899/) (2024)
- [Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data](https://mlsanity.com/paper/2507.22488/) (2025)

## 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.