# Training Green AI Models Using Elite Samples

arXiv: https://arxiv.org/abs/2402.12010 · HTML: https://mlsanity.com/paper/2402.12010/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 72 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Selection via Proxy: Efficient Data Selection for Deep Learning](https://mlsanity.com/paper/1906.11829/) (2019)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [A Systematic Review of Green AI](https://mlsanity.com/paper/2301.11047/) (2023)
- [Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning](https://mlsanity.com/paper/2302.08476/) (2023)
- [Data-Centric Green AI: An Exploratory Empirical Study](https://mlsanity.com/paper/2204.02766/) (2022)
- [Characterizing the Efficiency vs. Accuracy Trade-off for Long-Context NLP Models](https://mlsanity.com/paper/2204.07288/) (2022)
- [DCNNs on a Diet: Sampling Strategies for Reducing the Training Set Size](https://mlsanity.com/paper/1606.04232/) (2016)

## Built on by (most cited first, 4 of 4)

- [Choosing to Be Green: Advancing Green AI via Dynamic Model Selection](https://mlsanity.com/paper/2509.19996/) (2025)
- [The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design Trade-Offs in Ensemble Learning Systems](https://mlsanity.com/paper/2407.02914/) (2024)
- [Accelerating prototype selection with spatial abstraction](https://mlsanity.com/paper/2403.11020/) (2024)
- [BEExformer: A Fast Inferencing Binarized Transformer with Early Exits](https://mlsanity.com/paper/2412.05225/) (2024)

## Abstract (arXiv)

The substantial increase in AI model training has considerable environmental implications, mandating more energy-efficient and sustainable AI practices. On the one hand, data-centric approaches show great potential towards training energy-efficient AI models. On the other hand, instance selection methods demonstrate the capability of training AI models with minimised training sets and negligible performance degradation. Despite the growing interest in both topics, the impact of data-centric training set selection on energy efficiency remains to date unexplored. This paper presents an evolutionary-based sampling framework aimed at (i) identifying elite training samples tailored for datasets and model pairs, (ii) comparing model performance and energy efficiency gains against typical model training practice, and (iii) investigating the feasibility of this framework for fostering sustainable model training practices. To evaluate the proposed framework, we conducted an empirical experiment including 8 commonly used AI classification models and 25 publicly available datasets. The results showcase that by considering 10% elite training samples, the models' performance can show a 50% improvement and remarkable energy savings of 98% compared to the common training practice.