Summary of Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-based Thresholding, by Sofiane Laridi et al.
Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding
by Sofiane Laridi, Gregory Palmer, Kam-Ming Mark Tam
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper introduces a novel method for anomaly detection in Federated Learning (FL) settings, which leverages summary statistics from both normal and anomalous data to improve accuracy and robustness. The proposed approach aggregates local summary statistics across clients to compute a global threshold that optimally separates anomalies from normal data while preserving privacy. The authors conduct experiments on publicly available datasets, including Credit Card Fraud Detection, Shuttle, and Covertype, under various data distribution scenarios. The results demonstrate the method’s superiority over existing techniques, particularly in handling non-IID data distributions. The study also explores the impact of different data distribution scenarios and the number of clients on federated anomaly detection performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to find unusual patterns (anomalies) in a group of computers that are connected but don’t share their data directly. It’s hard to do this because the data might not be the same everywhere, which makes it tricky to figure out what’s normal and what’s not. The authors came up with a clever idea to use summary statistics from both normal and unusual data to make sure they’re getting accurate results while keeping things private. They tested their method on some real-life datasets and showed that it works better than other methods, especially when the data is different in each group. |
Keywords
» Artificial intelligence » Anomaly detection » Federated learning
