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. We present a practical carbon-accounting methodology for FL CO2e tracking using NVIDIA NVFlare and CodeCarbon for explicit, phase-aware tasks (initialization, per-round training, evaluation, and idle/coordination). To capture non-compute effects, we additionally estimate communication emissions fro
The increasing scale and distributed nature of AI model training, particularly federated learning, necessitates standardized methods to account for its environmental footprint.
As the energy consumption of AI becomes a critical concern, standardized carbon accounting for federated learning provides essential transparency and enables greener AI development, impacting regulatory considerations and investor sentiment.
The introduction of a practical carbon-accounting methodology will enable more consistent and comparable measurement of Federated Learning's environmental impact across different research and industry implementations.
- · Green AI initiatives
- · Companies committed to ESG reporting
- · Federated Learning platform developers
- · Environmental regulators
- · Opaque AI development practices
- · High-emission FL training approaches
- · Companies with poor environmental oversight
An immediate direct effect will be improved accountability and comparability of environmental metrics in federated learning projects.
This improved transparency could drive demand for energy-efficient federated learning algorithms and hardware, influencing R&D priorities.
Long-term, standardized green accounting in FL could contribute to broader industry-wide 'green' compute mandates or certifications, impacting global AI infrastructure strategy.
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Read at arXiv cs.AI