Establishing the Parameters of a Decentralized Neural Machine Learning Model

  • Aline Ioste USP
  • Marcelo Finger USP


The decentralized machine learning models face a bottleneck of high-cost communication. Trade-offs between communication and accuracy in decentralized learning have been addressed by theoretical approaches. Here we propose a new practical model that performs several local training operations before a communication round, choosing among several options. We show how to determine a configuration that dramatically reduces the communication burden between participant hosts, with a reduction in communication practice showing robust and accurate results both to IID and NON-IID data distributions.


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IOSTE, Aline; FINGER, Marcelo. Establishing the Parameters of a Decentralized Neural Machine Learning Model. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 19. , 2022, Campinas/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 401-412. ISSN 2763-9061. DOI: