Summary of Fedad-bench: a Unified Benchmark For Federated Unsupervised Anomaly Detection in Tabular Data, by Ahmed Anwar et al.
FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data
by Ahmed Anwar, Brian Moser, Dayananda Herurkar, Federico Raue, Vinit Hegiste, Tatjana Legler, Andreas Dengel
First submitted to arxiv on: 8 Aug 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 introduction of Federated Learning (FL) offers a promising approach for leveraging decentralized data while preserving privacy. Combining FL with anomaly detection is particularly compelling as it enables the detection of rare and critical anomalies in sensitive data from multiple sources, such as cybersecurity and healthcare. However, benchmarking the performance of anomaly detection methods in FL environments remains an underexplored area. This paper introduces FedAD-Bench, a unified benchmark for evaluating unsupervised anomaly detection algorithms within the context of FL. The authors systematically analyze and compare the performance of recent deep learning anomaly detection models under federated settings, which were typically assessed solely in centralized settings. FedAD-Bench encompasses diverse datasets and metrics to provide a holistic evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning (FL) is a way for many devices or computers to work together using data they each have, without sharing that data with anyone else. This helps keep the information private. FL can also be used to find unusual things in the data, like computer viruses or signs of illness in people. But right now, there isn’t a good way to test how well these programs do this job when working together. This paper makes a special tool called FedAD-Bench that lets us compare how different programs do at finding unusual things in FL. |
Keywords
» Artificial intelligence » Anomaly detection » Deep learning » Federated learning » Unsupervised
