Corpus ID: 222310239

A first look into the carbon footprint of federated learning

@article{Qiu2020AFL,
  title={A first look into the carbon footprint of federated learning},
  author={Xinchi Qiu and Titouan Parcollet and Daniel J. Beutel and Taner Topal and Akhil Mathur and N. Lane},
  journal={ArXiv},
  year={2020},
  volume={abs/2102.07627}
}
Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers. In response, alternatives to centralized training such as Federated Learning (FL) have emerged. Perhaps unexpectedly, FL in particular is starting to be deployed at a global scale by companies that must adhere to new legal demands and policies originating from governments and the civil society for privacy protection… Expand

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References

SHOWING 1-10 OF 47 REFERENCES
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
  • 218
  • PDF
Federated Optimization: Distributed Optimization Beyond the Datacenter
  • 244
  • Highly Influential
  • PDF
Quantifying the Carbon Emissions of Machine Learning
  • 38
  • Highly Influential
  • PDF
Towards Federated Learning: Robustness Analytics to Data Heterogeneity
  • 2
  • PDF
Energy and Policy Considerations for Deep Learning in NLP
  • 564
  • Highly Influential
  • PDF
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
  • T. Nishio, Ryo Yonetani
  • Computer Science
  • ICC 2019 - 2019 IEEE International Conference on Communications (ICC)
  • 2019
  • 250
  • PDF
...
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2
3
4
5
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