S. Savazzi, S. Kianoush, V. Rampa and M. Bennis, "A framework for energy and carbon footprint analysis of distributed and federated edge learning," 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Helsinki, Finland, 2021, pp. 1564-1569, doi: 10.1109/PIMRC50174.2021.9569307
A framework for energy and carbon footprint analysis of distributed and federated edge learning
|Author:||Savazzi, Stefano1; Kianoush, Sanaz1; Rampa, Vittorio1;|
1Consiglio Nazionale Delle Ricerche (CNR), IEIIT Institute, Milano
2Centre for Wireless Communications, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023042839338
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-04-28
Recent advances in distributed learning raise environmental concerns due to the large energy needed to train and move data to/from data centers. Novel paradigms, such as federated learning (FL), are suitable for decentralized model training across devices or silos that simultaneously act as both data producers and learners. Unlike centralized learning (CL) techniques, relying on big-data fusion and analytics located in energy hungry data centers, in FL scenarios devices collaboratively train their models without sharing their private data. This article breaks down and analyzes the main factors that influence the environmental footprint of FL policies compared with classical CL/Big-Data algorithms running in data centers. The proposed analytical framework takes into account both learning and communication energy costs, as well as the incurred greenhouse gas, or carbon equivalent, emissions. The framework is evaluated in an industrial setting assuming a real-world robotized workplace. Results show that FL allows remarkable end-to-end energy savings (30%÷40%) in low-rate/power IoT communications (with limited energy efficiency). On the other hand, FL is slower to converge when local data are unevenly distributed (often 2x slower than CL).
IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops
|Pages:||1564 - 1569|
2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
This work is partially supported by the EU CHIST-ERA project RadioSense, grant CHIST-ERA-17-BDSI-005.
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