Matching DNN Compression and Cooperative Training with Resources and Data Availability

To make machine learning (ML) sustainable and apt to run on the diverse devices where relevant data is, it is essential to compress ML models as needed, while still meeting the required learning quality and time performance. However, how much and when an ML model should be compressed, and where its...

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Bibliographic Details

Published in:	Annual Joint Conference of the IEEE Computer and Communications Societies pp. 1 - 10
Main Authors:	Malandrino, F., Giacomo, G. Di, Karamzade, A., Levorato, M., Chiasserini, C. F.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 17.05.2023
Subjects:	Adaptation models Approximation algorithms Costs Energy consumption Heuristic algorithms Prediction algorithms Training
ISSN:	2641-9874
Online Access:	Get full text
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