# FSL-BDP: Federated Survival Learning with Bayesian Differential Privacy for Credit Risk Modeling

arXiv: https://arxiv.org/abs/2601.11134 · HTML: https://mlsanity.com/paper/2601.11134/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 89 references that are in the map.
- First posted on arXiv 16 Jan 2026.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [A Scalable Discrete-Time Survival Model for Neural Networks](https://mlsanity.com/paper/1805.00917/) (2018)
- [Fairness in Credit Scoring: Assessment, Implementation and Profit Implications](https://mlsanity.com/paper/2103.01907/) (2021)
- [Federated Learning with Bayesian Differential Privacy](https://mlsanity.com/paper/1911.10071/) (2019)
- [The Value of Big Data for Credit Scoring: Enhancing Financial Inclusion using Mobile Phone Data and Social Network Analytics](https://mlsanity.com/paper/2002.09931/) (2020)
- [The value of text for small business default prediction: A deep learning approach](https://mlsanity.com/paper/2003.08964/) (2020)
- [Federated Survival Analysis with Discrete-Time Cox Models](https://mlsanity.com/paper/2006.08997/) (2020)
- [Federated Survival Forests](https://mlsanity.com/paper/2302.02807/) (2023)
- [Algorithmic decision making methods for fair credit scoring](https://mlsanity.com/paper/2209.07912/) (2022)
- [Gradient Boosting Survival Tree with Applications in Credit Scoring](https://mlsanity.com/paper/1908.03385/) (2019)
- [A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis](https://mlsanity.com/paper/2412.05164/) (2024)
- [PDx -- Adaptive Credit Risk Forecasting Model in Digital Lending using Machine Learning Operations](https://mlsanity.com/paper/2512.22305/) (2025)

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

- [BVFLMSP : Bayesian Vertical Federated Learning for Multimodal Survival with Privacy](https://mlsanity.com/paper/2604.02248/) (2026)
- [FedIncome: Federated Learning for Income Estimation in Digital Lending Under Data Sovereignty Constraints](https://mlsanity.com/paper/2609.27654/) (2026)

## Abstract (arXiv)

Credit risk models are a critical decision-support tool for financial institutions, yet tightening data-protection rules (e.g., GDPR, CCPA) increasingly prohibit cross-border sharing of borrower data, even as these models benefit from cross-institution learning. Traditional default prediction suffers from two limitations: binary classification ignores default timing, treating early defaulters (high loss) equivalently to late defaulters (low loss), and centralized training violates emerging regulatory constraints. We propose a Federated Survival Learning framework with Bayesian Differential Privacy (FSL-BDP) that models time-to-default trajectories without centralizing sensitive data. The framework provides Bayesian (data-dependent) differential privacy (DP) guarantees while enabling institutions to jointly learn risk dynamics. Experiments on three real-world credit datasets (LendingClub, SBA, Bondora) show that federation fundamentally alters the relative effectiveness of privacy mechanisms. While classical DP performs better than Bayesian DP in centralized settings, the latter benefits substantially more from federation (+7.0% vs +1.4%), achieving near parity of non-private performance and outperforming classical DP in the majority of participating clients. This ranking reversal yields a key decision-support insight: privacy mechanism selection should be evaluated in the target deployment architecture, rather than centralized benchmarks. These findings provide actionable guidance for practitioners designing privacy-preserving decision support systems in regulated, multi-institutional environments.