mlsanityData to 7 Oct 2026

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

arXiv 2208.12708 ↗First posted 26 Aug 2022cs.LG cs.CE cs.CRcited by 8

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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.

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

21 of 67 references.

References · 21

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2016Deep Learning with Differential Privacycited by 2,682
  4. 2020Extracting Training Data from Large Language Modelscited by 2,144
  5. 2017Federated Multi-Task Learningcited by 669
  6. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  7. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  8. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  9. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  10. 2021Opacus: User-Friendly Differential Privacy Library in PyTorchcited by 375
  11. 2016Semi-supervised Knowledge Transfer for Deep Learning from Private Training Datacited by 347
  12. 2021See through Gradients: Image Batch Recovery via GradInversioncited by 320
9 more
  1. 2018Distributed learning of deep neural network over multiple agentscited by 277
  2. 2017Efficient Private ERM for Smooth Objectivescited by 61
  3. 2017Differentially Private Empirical Risk Minimization with Input Perturbationcited by 19
  4. 2017Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networkscited by 18
  5. 2019Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networkscited by 13
  6. 2021Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Taskscited by 8
  7. 2020PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learningcited by 7
  8. 2019Adversarial Learning of Deepfakes in Accountingcited by 5
  9. 2022Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacycited by 1

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 4; 2025: 3.

8 papers.

  1. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  2. 2024FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generationcited by 7
  3. 2022Federated Continual Learning to Detect Accounting Anomalies in Financial Auditingcited by 4
  4. 2025Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approachcited by 4
  5. 2024Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesiscited by 2
  6. 2024Recovering Global Data Distribution Locally in Federated Learningcited by 0
  7. 2025FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendationscited by 0
  8. 2025Privacy Preserving Diffusion Models for Mixed-Type Tabular Data Generationcited by 0