Image Classification With Convolutional Neural Networks In MapReduce

Abstract

Deep learning (DL) techniques, more specifically Convolutional Neural Networks (CNNs), have become increasingly popular in advancing the field of data science and have had great successes in a wide array of applications including computer vision, speech, natural language processing and etc. However, the training process of CNNs is computationally intensive and high computational cost, especially when the dataset is huge. To overcome these obstacles, this paper takes advantage of distributed frameworks and cloud computing to develop a parallel CNN algorithm. MapReduce is a scalable and fault-tolerant data processing tool that was developed to provide significant improvements in large-scale data-intensive applications in clusters. A MapReduce-based CNN (MCNN) is developed in this work to tackle the task of image classification. In addition, the proposed MCNN adopted the idea of adding dropout layers in the networks to tackle the overfitting problem. Close examination of the implementation of MCNN as well as how the proposed algorithm accelerates learning are discussed and demonstrated through experiments. Results reveal high classification accuracy and significant improvements in speedup, scaleup and sizeup compared to the standard algorithms.

High-Fidelity Gradient Inversion in Distributed Learning

“…There are also some linear equation solving-based GIAs (Trieu et al 2017;Zhu and Blaschko 2021;Chen and Campbell 2021), where the adversaries aim to establish linear equations that capture the relationship between the input and gradients, then leveraging linear equation solver to determine the input. However, these methods usually assume that the target model is linear and they are limited on reconstructing data from batch averaged gradients.…”