arXiv AI June 12, 2026 By Austin Tapp, Holger R. Roth, Ziyue Xu, Abhijeet Parida, Hareem Nisar, Marius George Linguraru

Standardized Methods and Recommendations for Green Federated Learning

arXiv:2602. 00343v2 Announce Type: replace-cross Abstract: Federated learning (FL) enables collaborative model training over privacy-sensitive, distributed data, but its environmental impact is difficult to compare across studies due to inconsistent measurement boundaries and heterogeneous reporting.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 16

Green AI Carbon Optimizer: Carbon-Efficient Training Location Recommendation and Global AI Energy Demand Forecasting

arXiv:2606. 14707v1 Announce Type: cross Abstract: AI training and deployment consume substantial electricity, but carbon outcomes remain weakly integrated into routine model development decisions.

By Yuxin Chen (University of Helsinki, Finland), Hao Gao (Independent Researcher), Chujie Zou (University of Helsinki, Finland)

benchmarks

arXiv AI

Jun 10

Accounting for AI Inference in Corporate GHG Inventories: A Four-Tier Methodology for Scope 3 Category 1 Reporting

arXiv:2606. 10660v1 Announce Type: cross Abstract: AI inference services -- API subscriptions, enterprise chat tools, and SaaS products with embedded AI features -- fall unambiguously within Scope 3 Category 1 under the Corporate Sustainability Reporting Directive (CSRD), which requires disclosure for fiscal years starting January 2024.

By Guillermo Llopis (SOMA AI, Barcelona)

benchmarks

arXiv Computer Vision

4d ago

RA-CFGCache: From Branch-Level Criteria to Guided-Risk Control under Classifier-Free Guidance

arXiv:2609.36433v1 Announce Type: new Abstract: Diffusion models enable high-quality visual generation, but iterative denoising remains computationally expensive, especially under classifier-free gui...

By Yiming Liu, Ben Wan, Tongxuan Liu, Ao Wang, Yuqi Xiong, Fan Zhang, Hui Chen, Guiguang Ding

diffusion safety

arXiv Machine Learning

4d ago

Greenpixie's AI Token Methodology: Assessing the Energy, Water and CO2-eq Impact of AI Tokens for Open and Closed Weight Models

arXiv:2609.33965v2 Announce Type: replace-cross Abstract: We describe a methodology for estimating the per-token energy cost of cloud-hosted large language model (LLM) inference, separating between i...

By Joshua Horswill, Ross Hunter, Matt Clifford, James Hall

llms robotics benchmarks

arXiv AI

Aug 14

InFactPlanner: Planning Sustainable Geo-Distributed LLM Data Centers

arXiv:2608. 12915v1 Announce Type: cross Abstract: The rapid growth of LLM inference is shifting sustainability concerns from one-time training to continuous serving, where infrastructure decisions shape energy use, carbon emissions, water consumption, and service quality.

By Nicoletta Tsiopani, Moysis Symeonides, George Pallis, Marios D. Dikaiakos

llms

arXiv AI

Jul 3

The Rising Unsustainability of AI Graphics Cards Production

arXiv:2607. 01258v1 Announce Type: cross Abstract: The rapid advancement of Artificial Intelligence (AI) has been accompanied by significant increases in computational and environmental costs, driven by large-scale investments in AI infrastructure, hardware, and software.

By Cl\'ement Morand, Aur\'elie N\'ev\'eol, Anne-Laure Ligozat

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Green AI Carbon Optimizer: Carbon-Efficient Training Location Recommendation and Global AI Energy Demand Forecasting

Accounting for AI Inference in Corporate GHG Inventories: A Four-Tier Methodology for Scope 3 Category 1 Reporting

RA-CFGCache: From Branch-Level Criteria to Guided-Risk Control under Classifier-Free Guidance

Greenpixie's AI Token Methodology: Assessing the Energy, Water and CO2-eq Impact of AI Tokens for Open and Closed Weight Models

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The Rising Unsustainability of AI Graphics Cards Production