Improving IoT Analytics through Selective Edge Execution

Autor: Galanopoulos, A., Tasiopoulos, A. G., Iosifidis, G., Salonidis, T., Leith, D. J.

A large number of emerging IoT applications rely on machine learning routines for analyzing data. Executing such tasks at the user devices improves response time and economizes network resources. However, due to power and computing limitations, the d

Externí odkaz: http://arxiv.org/abs/2003.03588

Symmetric ADMM-Based Federated Learning with a Relaxed Step.

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Autor: Choudhury O; IBM Research Cambridge, Massachusetts, USA., Park Y; IBM Research Cambridge, Massachusetts, USA., Salonidis T; IBM T.J. Watson Research Center, Yorktown Heights, NY, USA., Gkoulalas-Divanis A; IBM Watson Health, Cambridge, Massachusetts, USA., Sylla I; IBM Research Cambridge, Massachusetts, USA., Das AK; IBM Research Cambridge, Massachusetts, USA.

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