Towards Quantifying the Carbon Emissions of Differentially Private
Machine Learning

Towards Quantifying the Carbon Emissions of Differentially Private Machine Learning

14 July 2021

Rakshit Naidu

Harshita Diddee

Krithika Ramesh

Ahmed S. Zamzam

Papers citing "Towards Quantifying the Carbon Emissions of Differentially Private Machine Learning"

5 / 5 papers shown

Title
Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey J. H. Liu Yao Du Kun Yang Yan Wang Xiping Hu Z. Wang Y. Liu Peng Sun Azzedine Boukerche Victor C.M. Leung 31 0 0 03 May 2025
FedZero: Leveraging Renewable Excess Energy in Federated Learning Philipp Wiesner R. Khalili Dennis Grinwald Pratik Agrawal L. Thamsen O. Kao 11 14 0 24 May 2023
Green Federated Learning Ashkan Yousefpour Sheng Guo Ashish Shenoy Sayan Ghosh Pierre Stock Kiwan Maeng Schalk-Willem Kruger Michael G. Rabbat Carole-Jean Wu Ilya Mironov FedML AI4CE 28 10 0 26 Mar 2023
Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning A. Luccioni Alex Hernandez-Garcia 19 45 0 16 Feb 2023
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 256 3,102 0 04 Nov 2016