# The Effects of Data Imbalance Under a Federated Learning Approach for Credit Risk Forecasting

arXiv: https://arxiv.org/abs/2401.07234 · HTML: https://mlsanity.com/paper/2401.07234/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 49 references that are in the map.
- First posted on arXiv 14 Jan 2024.

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

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [FetchSGD: Communication-Efficient Federated Learning with Sketching](https://mlsanity.com/paper/2007.07682/) (2020)
- [FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data](https://mlsanity.com/paper/2211.09299/) (2022)
- [Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging](https://mlsanity.com/paper/2205.08576/) (2022)
- [Federated Transfer Learning: concept and applications](https://mlsanity.com/paper/2010.15561/) (2020)
- [A Vertical Federated Learning Method for Interpretable Scorecard and Its Application in Credit Scoring](https://mlsanity.com/paper/2009.06218/) (2020)
- [Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates](https://mlsanity.com/paper/2304.07537/) (2023)
- [An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging](https://mlsanity.com/paper/2107.08371/) (2021)
- [Application of Deep Neural Networks to assess corporate Credit Rating](https://mlsanity.com/paper/2003.02334/) (2020)
- [Experimenting with Normalization Layers in Federated Learning on non-IID scenarios](https://mlsanity.com/paper/2303.10630/) (2023)
- [A Vertical Federated Learning Method For Multi-Institutional Credit Scoring: MICS](https://mlsanity.com/paper/2111.09038/) (2021)

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

- [A Thorough Assessment of the Non-IID Data Impact in Federated Learning](https://mlsanity.com/paper/2503.17070/) (2025)
- [Trans-XFed: An Explainable Federated Learning for Supply Chain Credit Assessment](https://mlsanity.com/paper/2508.13715/) (2025)

## Abstract (arXiv)

Credit risk forecasting plays a crucial role for commercial banks and other financial institutions in granting loans to customers and minimise the potential loss. However, traditional machine learning methods require the sharing of sensitive client information with an external server to build a global model, potentially posing a risk of security threats and privacy leakage. A newly developed privacy-preserving distributed machine learning technique known as Federated Learning (FL) allows the training of a global model without the necessity of accessing private local data directly. This investigation examined the feasibility of federated learning in credit risk assessment and showed the effects of data imbalance on model performance. Two neural network architectures, Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM), and one tree ensemble architecture, Extreme Gradient Boosting (XGBoost), were explored across three different datasets under various scenarios involving different numbers of clients and data distribution configurations. We demonstrate that federated models consistently outperform local models on non-dominant clients with smaller datasets. This trend is especially pronounced in highly imbalanced data scenarios, yielding a remarkable average improvement of 17.92% in model performance. However, for dominant clients (clients with more data), federated models may not exhibit superior performance, suggesting the need for special incentives for this type of clients to encourage their participation.