Fog-Embedded Deep Learning for the Internet of Things

Abstract

© 2005-2012 IEEE. In current deep learning models, centralized architecture forces participants to pool their data to the central Cloud to train a global model, while distributed architecture requires a parameter server to mediate the training process. However, privacy issues, response delays, and computation and communication bottlenecks prevent these architectures from working well at the scale of Internet of Things devices. To counter these problems, in this paper we build a Fog-embedded privacy-preserving deep learning framework (FPPDL), which moves computation from the centralized Cloud to Fog nodes near the end devices. The experimental results on benchmark image datasets under differe..