A Vertical Federated Learning Method For Multi-Institutional Credit Scoring: MICS
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Abstract (arXiv)
As more and more companies store their customers' data; various information of a person is distributed among numerous companies' databases. Different industrial sectors carry distinct features about the same customers. Also, different companies within the same industrial sector carry similar kinds of data about the customers with different data representations. Cooperation between companies from different industrial sectors, called vertical cooperation, and between the companies within the same sector, called horizontal cooperation, can lead to more accurate machine learning models and better estimations in tasks such as credit scoring. However, data privacy regulations and compatibility issues for different data representations are huge obstacles to cooperative model training. By proposing the training framework MICS and experimentation on several numerical data sets, we showed that companies would have an incentive to cooperate with other companies from their sector and with other industrial sectors to jointly train more robust and accurate global models without explicitly sharing their customers' private data.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 28 references that are in the map. First posted on arXiv 17 Nov 2021.
Builds on
14 of 28 references.
References · 14
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- 2020Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Datacited by 35
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- 2020Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learningcited by 19
- 2020A Vertical Federated Learning Method for Interpretable Scorecard and Its Application in Credit Scoringcited by 16
- 2020Faster Secure Data Mining via Distributed Homomorphic Encryptioncited by 5
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1.
2 papers.