{"_id":"xnMRXKekB3JPxRnPT","bibbaseid":"savazzi-kianoush-rampa-bennis-aframeworkforenergyandcarbonfootprintanalysisofdistributedandfederatededgelearning-2021","author_short":["Savazzi, S.","Kianoush, S.","Rampa, V.","Bennis, M."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"A framework for energy and carbon footprint analysis of distributed and federated edge learning","author":[{"propositions":[],"lastnames":["Savazzi"],"firstnames":["Stefano"],"suffixes":[]},{"propositions":[],"lastnames":["Kianoush"],"firstnames":["Sanaz"],"suffixes":[]},{"propositions":[],"lastnames":["Rampa"],"firstnames":["Vittorio"],"suffixes":[]},{"propositions":[],"lastnames":["Bennis"],"firstnames":["Mehdi"],"suffixes":[]}],"booktitle":"2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","pages":"1564–1569","year":"2021","project":"radiosense","organization":"IEEE","bibtex":"@inproceedings{savazzi2021framework,\n title={A framework for energy and carbon footprint analysis of distributed and federated edge learning},\n author={Savazzi, Stefano and Kianoush, Sanaz and Rampa, Vittorio and Bennis, Mehdi},\n booktitle={2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)},\n pages={1564--1569},\n year={2021},\n project = {radiosense},\n organization={IEEE}\n}\n\n","author_short":["Savazzi, S.","Kianoush, S.","Rampa, V.","Bennis, M."],"key":"savazzi2021framework","id":"savazzi2021framework","bibbaseid":"savazzi-kianoush-rampa-bennis-aframeworkforenergyandcarbonfootprintanalysisofdistributedandfederatededgelearning-2021","role":"author","urls":{},"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"http://ambientintelligence.aalto.fi/bibtex/LiteraturAll","dataSources":["aPfcTvMp5jE2KuS7H","a6QYyvmdLfrsx7DiL"],"keywords":[],"search_terms":["framework","energy","carbon","footprint","analysis","distributed","federated","edge","learning","savazzi","kianoush","rampa","bennis"],"title":"A framework for energy and carbon footprint analysis of distributed and federated edge learning","year":2021}