Authors: Jieming Bian, Lei Wang, Shaolei Ren, Jie Xu

Published on: November 06, 2023

Impact Score: 8.35

Arxiv code: Arxiv:2311.03615

Summary

• What is new: The introduction of the CAFE framework to optimize AI model training across geo-distributed data centers with a focus on minimizing carbon footprint while maintaining learning performance.
• Why this is important: The significant computational power and energy required for training large-scale AI models increase the carbon footprint and potential environmental repercussions.
• What the research proposes: CAFE employs Federated Learning, coreset selection, and a novel algorithm using the Lyapunov drift-plus-penalty framework to optimize training within a carbon footprint budget efficiently.
• Results: The algorithm demonstrates superior performance in optimizing learning outcomes while minimizing environmental impact, outperforming existing methods in simulations using real-world data.

Technical Details

Technological frameworks used: CAFE (Carbon-Aware Federated Learning), Federated Learning, Lyapunov drift-plus-penalty

Models used: Coreset selection algorithm

Data used: Real-world carbon intensity data

Potential Impact

Tech companies focusing on AI model training, cloud service providers, environmental sustainability sectors

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