Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 12·5 min read
Standardized Methods and Recommendations for Green Federated Learning
TL;DR
Federated learning (FL) faces challenges in measuring its environmental impact due to inconsistent reporting methods. A carbon-accounting methodology was developed using NVIDIA NVFlare and CodeCarbon to track CO2 emissions during FL tasks.
✦ Why It Matters
Engineers can adopt standardized carbon accounting methods to evaluate and reduce the environmental impact of federated learning.
Key Takeaways
- Implement standardized carbon accounting in FL projects.
- Use NVIDIA NVFlare and CodeCarbon for emissions tracking.
- Evaluate system efficiencies to minimize carbon footprints.
- Report emissions per site and per round for accuracy.
Full Summary
Federated learning (FL) allows multiple parties to collaboratively train machine learning models while keeping their data private, but measuring its environmental impact is complicated by varying methodologies. To address this, a carbon-accounting methodology was created using NVIDIA NVFlare and CodeCarbon, focusing on specific phases of FL such as initialization, training, and evaluation.
The study also estimated emissions from communication based on model-update sizes. Testing on CIFAR-10 image classification revealed that inefficiencies could increase CO2 emissions by up to 21.73 times compared to a high-efficiency baseline.
In retinal optic disk segmentation, different GPU tiers showed a consistent runtime gap of 1.7 times, with varying energy and CO2 emissions across sites. These findings emphasize the importance of per-site and per-round reporting for accurate carbon accounting in FL.
Overall, the proposed methodology supports reproducible evaluations of 'green' FL practices.