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Accelerating Distributed Learning in Non-Dedicated Environments

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2 Published in 2023 at "IEEE Transactions on Cloud Computing"

DOI: 10.1109/tcc.2021.3102593

Abstract: Machine learning (ML) models are increasingly trained with distributed workers possessing heterogeneous resources. In such scenarios, model training efficiency may be negatively affected by stragglers—workers that run much slower than others. Efficient model training requires… read more here.

Keywords: accelerating distributed; learning non; load; dedicated environments ... See more keywords