Summary of Towards Sustainable Secureml: Quantifying Carbon Footprint Of Adversarial Machine Learning, by Syed Mhamudul Hasan et al.
Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning
by Syed Mhamudul Hasan, Abdur R. Shahid, Ahmed Imteaj
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 Medium Difficulty summary: This paper investigates the environmental impact of machine learning (ML) models, specifically those designed to be robust against adversarial attacks. The authors find that increasing model robustness leads to higher energy consumption and carbon emissions, highlighting a trade-off between security and sustainability. To quantify this relationship, they introduce the Robustness Carbon Trade-off Index (RCTI), which measures the sensitivity of carbon emissions to changes in adversarial robustness. The study demonstrates the RCTI through an experiment involving evasion attacks, analyzing the interplay between robustness against attacks, performance, and carbon emissions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research looks at how machine learning models affect the environment. It found that making models more secure can actually make them use more energy and create more pollution. The researchers created a new way to measure this trade-off called the Robustness Carbon Trade-off Index (RCTI). They tested it by analyzing how well a model could resist attacks while also considering its impact on the environment. |
Keywords
* Artificial intelligence * Machine learning
