Tuning DNN Model Compression to Resource and Data Availability in Cooperative Training

Model compression is a fundamental tool to execute machine learning (ML) tasks on the diverse set of devices populating current-and next-generation networks, thereby exploiting their resources and data. At the same time, how much and when to compress ML models are very complex decisions, as they hav...

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Veröffentlicht in:	IEEE/ACM transactions on networking Jg. 32; H. 2; S. 1 - 16
Hauptverfasser:	Malandrino, Francesco, di Giacomo, Giuseppe, Karamzade, Armin, Levorato, Marco, Chiasserini, Carla Fabiana
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Alternative energy sources Availability Complexity Computational modeling Costs Data compression Data models Decisions Distributed learning Dynamic programming Energy consumption Energy costs Graph representations Graphical representations Machine learning model pruning Network latency network support to machine learning Optimization Polynomials Switches Task analysis Training
ISSN:	1063-6692, 1558-2566
Online-Zugang:	Volltext
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