Establishing the Parameters of a Decentralized Neural Machine Learning Model - Ioste, Aline; Finger, Marcelo (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2022)
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.