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cs.LG2024
Dependable Distributed Training of Compressed Machine Learning Models
Francesco Malandrino, Giuseppe Di Giacomo, Marco Levorato +1
The existing work on the distributed training of machine learning (ML) models has consistently overlooked the distribution of the achieved learning quality, focusing instead on its…
cs.LG2022★ 1 cited
Matching DNN Compression and Cooperative Training with Resources and Data Availability
Francesco Malandrino, Giuseppe Di Giacomo, Armin Karamzade +2
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 req…