# A Vertical Federated Learning Method For Multi-Institutional Credit Scoring: MICS

arXiv: https://arxiv.org/abs/2111.09038 · HTML: https://mlsanity.com/paper/2111.09038/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 17 Nov 2021.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Privacy Preserving Vertical Federated Learning for Tree-based Models](https://mlsanity.com/paper/2008.06170/) (2020)
- [VAFL: a Method of Vertical Asynchronous Federated Learning](https://mlsanity.com/paper/2007.06081/) (2020)
- [Privacy Preserving Machine Learning: Threats and Solutions](https://mlsanity.com/paper/1804.11238/) (2018)
- [Federated Learning with Bayesian Differential Privacy](https://mlsanity.com/paper/1911.10071/) (2019)
- [Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data](https://mlsanity.com/paper/2008.06197/) (2020)
- [Experiments with Random Projection](https://mlsanity.com/paper/1301.3849/) (2013)
- [Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning](https://mlsanity.com/paper/2008.06233/) (2020)
- [A Vertical Federated Learning Method for Interpretable Scorecard and Its Application in Credit Scoring](https://mlsanity.com/paper/2009.06218/) (2020)
- [Faster Secure Data Mining via Distributed Homomorphic Encryption](https://mlsanity.com/paper/2006.10091/) (2020)
- [Optimization for Large-Scale Machine Learning with Distributed Features and Observations](https://mlsanity.com/paper/1610.10060/) (2016)
- [Stochastic Distributed Optimization for Machine Learning from Decentralized Features](https://mlsanity.com/paper/1812.06415/) (2018)

## Built on by (most cited first, 2 of 2)

- [Vertical Federated Learning: A Structured Literature Review](https://mlsanity.com/paper/2212.00622/) (2022)
- [The Effects of Data Imbalance Under a Federated Learning Approach for Credit Risk Forecasting](https://mlsanity.com/paper/2401.07234/) (2024)

## 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.