Summary of Fin-fed-od: Federated Outlier Detection on Financial Tabular Data, by Dayananda Herurkar et al.
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data
by Dayananda Herurkar, Sebastian Palacio, Ahmed Anwar, Joern Hees, Andreas Dengel
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel approach combines representation learning and federated learning techniques to enhance outlier detection within individual organizations without compromising data confidentiality. This is achieved by leveraging latent representations obtained from client-owned autoencoders to refine the decision boundary of inliers, while only sharing model parameters between organizations. The proposed method demonstrates a strong improvement in the classification of unknown outliers during the inference phase for each organization’s model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem that happens when we try to find unusual things (called anomalies) in real-world situations. It’s hard because these anomalies can be different every time, and we don’t always know what they look like. In addition, sometimes other companies have information about anomalies, but they’re not allowed to share it because it’s private or might give them an unfair advantage. The paper comes up with a new way to find unknown anomalies that keeps the information private. They use special computer techniques called representation learning and federated learning to help machines make better decisions. They test their method on some financial data and image data, and it works really well! |
Keywords
» Artificial intelligence » Classification » Federated learning » Inference » Outlier detection » Representation learning
