Federated learning — environmental sustainability

Environmental sustainability

Carbon footprint monitoring

Measurement tools:

• CodeCarbon: Python package for tracking CO₂ emissions [1].

  from codecarbon import EmissionsTracker
  tracker = EmissionsTracker()
  tracker.start()
  # Run FL training
  emissions = tracker.stop()
  

• ML CO2 Impact: Online calculator for ML carbon footprint.
• Green Algorithms: Computational footprint calculator.

Optimisation strategies:

• Schedule training during low-carbon energy periods.
• Use model compression techniques (pruning, quantization).
• Implement early stopping based on carbon budget.
• Prefer edge devices over cloud GPUs when possible.
• Adaptive client‑selection (EcoLearn) cuts CO₂ by up to ten × without accuracy loss [2].

See: EcoFL framework (arXiv 2023).

Bibliography

1. Benoit Courty and Victor Schmidt and Sasha Luccioni and Goyal-Kamal and MarionCoutarel and Boris Feld and Jérémy Lecourt and LiamConnell and Amine Saboni and Inimaz and supatomic and Mathilde Léval and Luis Blanche and Alexis Cruveiller and ouminasara and Franklin Zhao and Aditya Joshi and Alexis Bogroff and Hugues de Lavoreille and Niko Laskaris and Edoardo Abati and Douglas Blank and Ziyao Wang and Armin Catovic and Marc Alencon and Michał Stęchły and Christian Bauer and Lucas Otávio N. de Araújo and JPW and MinervaBooks mlco2/codecarbon: v2.4.1. DOI: 10.5281/zenodo.11171501

2. Mehboob, Talha, Bashir, Noman, Iglesias, Jesus Omana, Zink, Michael, Irwin, David (2023). CEFL: Carbon-efficient federated learning. arXiv preprint arXiv:2310.17972. Available at: https://arxiv.org/abs/2310.17972

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Original repository: federated_learning_page