S. Savazzi, V. Rampa, S. Kianoush and M. Bennis, "An Energy and Carbon Footprint Analysis of Distributed and Federated Learning," in IEEE Transactions on Green Communications and Networking, vol. 7, no. 1, pp. 248-264, March 2023, doi: 10.1109/TGCN.2022.3186439
An energy and carbon footprint analysis of distributed and federated learning
|Author:||Savazzi, Stefano1; Rampa, Vittorio1; Kianoush, Sanaz1;|
1Consiglio Nazionale delle Ricerche, Institute of Electronics, Information Engineering and Telecommunications (IEIIT), 20133 Milan, Italy
2Centre for Wirelesss Communication, University of Oulu, 90570 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 2.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023032032395
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-03-20
Classical and centralized Artificial Intelligence (AI) methods require moving data from producers (sensors, machines) to energy hungry data centers, raising environmental concerns due to computational and communication resource demands, while violating privacy. Emerging alternatives to mitigate such high energy costs propose to efficiently distribute, or federate, the learning tasks across devices, which are typically low-power. This paper proposes a novel framework for the analysis of energy and carbon footprints in distributed and federated learning (FL). The proposed framework quantifies both the energy footprints and the carbon equivalent emissions for vanilla FL methods and consensus-based fully decentralized approaches. We discuss optimal bounds and operational points that support green FL designs and underpin their sustainability assessment. Two case studies from emerging 5G industry verticals are analyzed: these quantify the environmental footprints of continual and reinforcement learning setups, where the training process is repeated periodically for continuous improvements. For all cases, sustainability of distributed learning relies on the fulfillment of specific requirements on communication efficiency and learner population size. Energy and test accuracy should be also traded off considering the model and the data footprints for the targeted industrial applications.
IEEE transactions on green communications and networking
|Pages:||248 - 264|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported by the CHIST-ERA European Projects CONNECT (Communication Aware Dynamic Edge Computing) and RadioSense (Wireless Big-Data Augmented Smart Industry).
© The Author(s) 2022. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0.