Summary of Support Vector Based Anomaly Detection in Federated Learning, by Massimo Frasson and Dario Malchiodi
Support Vector Based Anomaly Detection in Federated Learning
by Massimo Frasson, Dario Malchiodi
First submitted to arxiv on: 4 Jul 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 proposes two innovative algorithms, Ensemble SVDD and Support Vector Election, for anomaly detection in a federated learning setting. These algorithms leverage Support Vector Machines (SVMs) to detect anomalies in decentralized systems, offering an alternative to Neural Networks typically used in Federated Learning. The proposed methods operate effectively with small datasets and incur lower computational costs compared to traditional approaches. Initial results demonstrate promising performance across various distributed system configurations, paving the way for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding unusual patterns in data that comes from different sources. Right now, this task is mostly done by big computers that collect all the data first. However, this approach has some problems, like keeping people’s private information safe. A new way to do this called Federated Learning might help solve these issues. The authors of this paper created two new algorithms that can find unusual patterns in smaller pieces of data and don’t need as much computer power. They tested these algorithms on different systems and got promising results. |
Keywords
* Artificial intelligence * Anomaly detection * Federated learning
