# Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits

arXiv: https://arxiv.org/abs/2208.12708 · HTML: https://mlsanity.com/paper/2208.12708/

## Key facts

- Cited by 8 papers in the map (data through 7 Oct 2026).
- Builds on 21 of its 67 references that are in the map.
- First posted on arXiv 26 Aug 2022.

## Builds on (most cited first, 21 of 21)

- [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)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [Opacus: User-Friendly Differential Privacy Library in PyTorch](https://mlsanity.com/paper/2109.12298/) (2021)
- [Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data](https://mlsanity.com/paper/1610.05755/) (2016)
- [See through Gradients: Image Batch Recovery via GradInversion](https://mlsanity.com/paper/2104.07586/) (2021)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Efficient Private ERM for Smooth Objectives](https://mlsanity.com/paper/1703.09947/) (2017)
- [Differentially Private Empirical Risk Minimization with Input Perturbation](https://mlsanity.com/paper/1710.07425/) (2017)
- [Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks](https://mlsanity.com/paper/1709.05254/) (2017)
- [Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks](https://mlsanity.com/paper/1908.00734/) (2019)
- [Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks](https://mlsanity.com/paper/2109.11201/) (2021)
- [PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning](https://mlsanity.com/paper/2010.02529/) (2020)
- [Adversarial Learning of Deepfakes in Accounting](https://mlsanity.com/paper/1910.03810/) (2019)
- [Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy](https://mlsanity.com/paper/2204.13697/) (2022)

## Built on by (most cited first, 8 of 8)

- [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024)
- [FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation](https://mlsanity.com/paper/2401.06263/) (2024)
- [Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing](https://mlsanity.com/paper/2210.15051/) (2022)
- [Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach](https://mlsanity.com/paper/2501.12723/) (2025)
- [Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis](https://mlsanity.com/paper/2412.16083/) (2024)
- [Recovering Global Data Distribution Locally in Federated Learning](https://mlsanity.com/paper/2409.14063/) (2024)
- [FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations](https://mlsanity.com/paper/2505.18177/) (2025)
- [Privacy Preserving Diffusion Models for Mixed-Type Tabular Data Generation](https://mlsanity.com/paper/2512.00638/) (2025)

## Abstract (arXiv)

The ongoing 'digital transformation' fundamentally changes audit evidence's nature, recording, and volume. Nowadays, the International Standards on Auditing (ISA) requires auditors to examine vast volumes of a financial statement's underlying digital accounting records. As a result, audit firms also 'digitize' their analytical capabilities and invest in Deep Learning (DL), a successful sub-discipline of Machine Learning. The application of DL offers the ability to learn specialized audit models from data of multiple clients, e.g., organizations operating in the same industry or jurisdiction. In general, regulations require auditors to adhere to strict data confidentiality measures. At the same time, recent intriguing discoveries showed that large-scale DL models are vulnerable to leaking sensitive training data information. Today, it often remains unclear how audit firms can apply DL models while complying with data protection regulations. In this work, we propose a Federated Learning framework to train DL models on auditing relevant accounting data of multiple clients. The framework encompasses Differential Privacy and Split Learning capabilities to mitigate data confidentiality risks at model inference. We evaluate our approach to detect accounting anomalies in three real-world datasets of city payments. Our results provide empirical evidence that auditors can benefit from DL models that accumulate knowledge from multiple sources of proprietary client data.