arXiv:2608. 14359v1 Announce Type: new Abstract: The sustainability constraints of FLaaS consumers pose significant challenges to maintaining carbon-feasible federated training in FLaaS environments.
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arXiv:2608. 09998v1 Announce Type: new Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have become powerful tools for supporting and automating complex human tasks.
arXiv:2609.15389v1 Announce Type: cross Abstract: As artificial intelligence systems increasingly rely on distributed and collaborative training, the energy footprint of these processes becomes a sha...
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.
The paper highlights that as Large Language Models grow in capability and prevalence, their environmental footprint is increasing, yet the machine learning community lacks standardized carbon accounting practices. An automated review of 5,285 NeurIPS 2025 papers shows almost no reporting of environmental impact. To address this, the authors propose standardized sustainability metrics for training efficiency, heuristics for estimating inference carbon costs, a software tool called carbonbenchmark for tracking emissions, and the SMAJ framework to encourage prioritizing computational efficiency and environmental accountability over marginal accuracy gains.
arXiv:2602. 15423v4 Announce Type: replace-cross Abstract: As the burgeoning power requirements of sophisticated neural architectures escalate, the information retrieval community has recognized ecological sustainability as a pivotal priority that necessitates a fundamental paradigm shift in model design.
The paper presents a carbon‑aware routing framework for function‑calling in large language models that distributes queries across a three‑tier edge‑cloud architecture. A lightweight k‑NN predictor estimates accuracy, delay, and power for each edge tier, and real‑time grid carbon intensity is used to route queries to the lowest‑emission tier that can execute them. Experiments on state‑of‑the‑art benchmarks show the framework matches cloud‑level accuracy while cutting operational carbon emissions by an average of four times.