A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification-Reference-Cited by-同舟云学术

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A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification

Published:2023-11-06 Issue: Volume:97 Page:213-218
ISSN:
Container-title:2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)
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Author:

Barbieri Luca¹,Savazzi Stefano²,Kianoush Sanaz²,Nicoli Monica¹,Serio Luigi³

Affiliation:

1. Politecnico di Milano,Milan,Italy

2. Consiglio Nazionale delle Ricerche,Milan,Italy

3. CERN,Technology Department,Geneva 23,Switzerland,1211

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10478373/10478374/10478391.pdf?arnumber=10478391

Reference24 articles.

1. Towards the systematic reporting of the energy and carbon footprints of machine learning;Henderson,2020

2. Advances and open problems in federated learning;Kairouz;Foundations and Trends in Machine Learning,2021

3. Opportunities of Federated Learning in Connected, Cooperative, and Automated Industrial Systems

4. A first look into the carbon footprint of federated learning;Qiu,2021

5. Energy Efficient Federated Learning Over Wireless Communication Networks

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Compressed Bayesian Federated Learning for Reliable Passive Radio Sensing in Industrial IoT;2024 IEEE Conference on Artificial Intelligence (CAI);2024-06-25

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