In this paper, we study how to enable deep learning without disclosing individual data to the DNN trainer. We analyze the risks in conventional deep learning ...
Abstract— Deep Learning has shown promising performance in a variety of pattern recognition tasks owning to large quantities of training data and complex ...
Aug 23, 2018 · Crowdlearning: Crowded Deep Learning with Data Privacy ; graphical distance, social network, users' habits, etc..W.l.o.g., ; we assume grouping is ...
Abstract— Deep Learning has shown promising performance in a variety of pattern recognition tasks owning to large quantities of training data and complex ...
This paper proposes a novel idea - Crowdlearning, which decentralizes the heavy- load training procedure and deploys the training into a crowd of ...
In this paper, we study how to enable deep learning without disclosing individual data to the DNN trainer. We analyze the risks in conventional deep learning ...
In this paper, we study how to enable deep learning without disclosing individual data to the DNN trainer. We analyze the risks in conventional deep learning ...
This paper presents Crowd-ML, a privacy-preserving machine learning framework for a crowd of smart devices, which can solve a wide range of learning ...
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At a given sensing cycle, each expert (a deep neural network DDA algorithm) independently labels all the unseen data samples. The output of each expert is ...
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Sep 6, 2017 · In this paper, we address the problem of learning deep neural networks from crowds. We begin by describing an EM algorithm for jointly learning ...