An Energy and Carbon Footprint Analysis of Distributed and Federated Learning

Author:

Savazzi Stefano1ORCID,Rampa Vittorio1ORCID,Kianoush Sanaz1ORCID,Bennis Mehdi2ORCID

Affiliation:

1. Consiglio Nazionale delle Ricerche, Institute of Electronics, Information Engineering and Telecommunications (IEIIT), Milan, Italy

2. Centre for Wirelesss Communication, University of Oulu, Oulu, Finland

Funder

CHIST-ERA European Projects CONNECT (Communication-Aware Dynamic Edge Computing) and RadioSense

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Renewable Energy, Sustainability and the Environment

Reference55 articles.

1. GADMM: Fast and communication efficient framework for distributed machine learning;elgabli;J Mach Learn Res,2020

2. FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization;reisizadeh;Proc Int Conf Artif Intell Stat,2020

3. Gossip training for deep learning;blot;Proc 30th Conf Neural Inf Process Syst (NIPS),2016

4. Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air

5. A Software Architecture Enabling the Web of Things

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