# Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data

arXiv: https://arxiv.org/abs/2404.14933 · HTML: https://mlsanity.com/paper/2404.14933/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 50 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Deep Learning for Financial Applications : A Survey](https://mlsanity.com/paper/2002.05786/) (2020)
- [Learning to Detect Malicious Clients for Robust Federated Learning](https://mlsanity.com/paper/2002.00211/) (2020)
- [Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach](https://mlsanity.com/paper/2007.09712/) (2020)
- [Free-riders in Federated Learning: Attacks and Defenses](https://mlsanity.com/paper/1911.12560/) (2019)
- [Abnormal Client Behavior Detection in Federated Learning](https://mlsanity.com/paper/1910.09933/) (2019)
- [DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection](https://mlsanity.com/paper/2106.05410/) (2021)
- [Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks](https://mlsanity.com/paper/1709.05254/) (2017)
- [A Federated Learning Approach to Anomaly Detection in Smart Buildings](https://mlsanity.com/paper/2010.10293/) (2020)
- [Robust Federated Learning with Attack-Adaptive Aggregation](https://mlsanity.com/paper/2102.05257/) (2021)
- [Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks](https://mlsanity.com/paper/1908.00734/) (2019)
- [Fast Deep Autoencoder for Federated learning](https://mlsanity.com/paper/2206.05136/) (2022)
- [RECol: Reconstruction Error Columns for Outlier Detection](https://mlsanity.com/paper/2102.02791/) (2021)
- [Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data](https://mlsanity.com/paper/2209.10658/) (2022)
- [Provable benefits of representation learning](https://mlsanity.com/paper/1706.04601/) (2017)
- [Solving the "false positives" problem in fraud prediction](https://mlsanity.com/paper/1710.07709/) (2017)
- [Latent-Insensitive autoencoders for Anomaly Detection](https://mlsanity.com/paper/2110.13101/) (2021)

## Built on by (most cited first, 3 of 3)

- [FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data](https://mlsanity.com/paper/2408.04442/) (2024)
- [Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking](https://mlsanity.com/paper/2504.20900/) (2025)
- [TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data](https://mlsanity.com/paper/2606.11844/) (2026)

## Abstract (arXiv)

Anomaly detection in real-world scenarios poses challenges due to dynamic and often unknown anomaly distributions, requiring robust methods that operate under an open-world assumption. This challenge is exacerbated in practical settings, where models are employed by private organizations, precluding data sharing due to privacy and competitive concerns. Despite potential benefits, the sharing of anomaly information across organizations is restricted. This paper addresses the question of enhancing outlier detection within individual organizations without compromising data confidentiality. We propose a novel method leveraging representation learning and federated learning techniques to improve the detection of unknown anomalies. Specifically, our approach utilizes latent representations obtained from client-owned autoencoders to refine the decision boundary of inliers. Notably, only model parameters are shared between organizations, preserving data privacy. The efficacy of our proposed method is evaluated on two standard financial tabular datasets and an image dataset for anomaly detection in a distributed setting. The results demonstrate a strong improvement in the classification of unknown outliers during the inference phase for each organization's model.