Summary of Sensitivity Curve Maximization: Attacking Robust Aggregators in Distributed Learning, by Christian A. Schroth et al.
Sensitivity Curve Maximization: Attacking Robust Aggregators in Distributed Learning
by Christian A. Schroth, Stefan Vlaski, Abdelhak M. Zoubir
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 Distributed learning agents aim to collaborate on global problems, but increasing network size makes it likely that individual agents are malicious or faulty. This can cause the learning process to degenerate or break down. Classical aggregation schemes fail at small contamination rates, prompting a search for robust alternatives. While these robust aggregators can tolerate higher contamination rates, many are vulnerable to carefully crafted attacks. Our work shows how the sensitivity curve (SC) from robust statistics can be used to derive optimal attack patterns against arbitrary robust aggregators, often rendering them ineffective. We demonstrate this attack’s effectiveness through multiple simulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a team of computers working together to solve a big problem. But what if some of those computers are not trustworthy? This can cause the whole process to fail. To fix this, we need better ways to combine information from all the computers. Some of these methods are good at dealing with small amounts of fake data, but they can still be tricked by clever attacks. Our research shows how to use a special tool called the sensitivity curve to find the best way to attack these robust aggregators and make them useless. |
Keywords
» Artificial intelligence » Prompting
