Entity Resolution and Federated Learning get a Federated Resolution

By Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Giorgio Patrini, Guillaume Smith, Brian Thorne. 2018

95 citations recorded by the source corpus.

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 \textit{does 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.

Subfields: Artificial Intelligence, Management Science and Operations Research, Information Extraction, Federated & Distributed Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Qiang Yang
2. Yang Liu
3. Murali Annavaram
4. Chaoyang He
5. Bingsheng He
6. Qinbin Li
7. S. Avestimehr
8. Tianjian Chen

In Management Science and Operations Research

1. Hangyu Zhu
2. Murali Annavaram
3. Tianjian Chen
4. Bingsheng He
5. Peilin Zhao
6. Ramesh Raskar
7. Li Shen
8. Qiang Yang

In Information Extraction

1. Yang Liu

In Federated & Distributed Learning

1. Qiang Yang
2. Yang Liu
3. Murali Annavaram
4. Bingsheng He
5. Qinbin Li
6. Chaoyang He
7. Tianjian Chen
8. Zeyi Wen

Citations in the corpus

• SplitNN-driven Vertical Partitioning (2020, background): “Vertical federated learning Learning with vertically partitioned data in the context of federated learning, a popular distributed deep learning paradigm was studied in (Nock et al. , 2018; Liu et al. , 2019, 2020; Sameer et al. , 2018; Vepakomma et al. , 2018b; Sharma et al. , 2019a,b).”
• FedML: A Research Library and Benchmark for Federated Machine Learning (2020, background): “As demonstrated in Figure 3, FL has various topology definitions, such as vertical FL [34, 35, 36, 37, 38, 39, 40], split learning [41, 42], decentralized FL [43, 44, 45, 46], and Hierarchical FL [47, 48, 49, 50, 51, 52].”
• Federated Machine Learning (2019, methodology): “ly partitioned data, including Cooperative Statistical Analysis [15], association rule mining [65], secure linear regression [22, 32, 55], classification [16] and gradient descent [68]. Recently, Ref [27, 49] proposed a vertical federated learning scheme to train a privacy-preserving logistic regres”
• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, methodology): “Applying the above methods, the user privacy of FLSs can usually be well protected [89, 211, 91, 144].”
• A Survey on federated learning* (2020, methodology): “There comes a modified token-based entity resolution algorithm to preprocess vertical partitioned data, powered by Nock et al. [15].”
• Secure and Efficient Federated Transfer Learning (2019, background): “data partitioned in the feature space) has also been studied in depth [5], [6].”
• Federated Learning on Non-IID Data: A Survey (2021, background): “[102] provide a detailed analysis of the impact of identity resolution.”
• Cloud-based Federated Boosting for Mobile Crowdsensing (2020, unknown): “In [17], Richard et al.”