scholarly journals Mobile Edge Computing Enabled Efficient Communication Based on Federated Learning in Internet of Medical Things

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiao Zheng ◽  
Syed Bilal Hussain Shah ◽  
Xiaojun Ren ◽  
Fengqi Li ◽  
Liqaa Nawaf ◽  
...  
Keyword(s):  
Gradient Descent ◽  
Home Health ◽  
Edge Computing ◽  
Communication Cost ◽  
Stochastic Gradient Descent ◽  
Machine Learning Model ◽  
Efficient Communication ◽  
Internet Of Medical Things ◽  
Number Of Iterations ◽  
Optimal Machine

The rapid growth of the Internet of Medical Things (IoMT) has led to the ubiquitous home health diagnostic network. Excessive demand from patients leads to high cost, low latency, and communication overload. However, in the process of parameter updating, the communication cost of the system or network becomes very large due to iteration and many participants. Although edge computing can reduce latency to some extent, there are significant challenges in further reducing system latency. Federated learning is an emerging paradigm that has recently attracted great interest in academia and industry. The basic idea is to train a globally optimal machine learning model among all participating collaborators. In this paper, a gradient reduction algorithm based on federated random variance is proposed to reduce the number of iterations between the participant and the server from the perspective of the system while ensuring the accuracy, and the corresponding convergence analysis is given. Finally, the method is verified by linear regression and logistic regression. Experimental results show that the proposed method can significantly reduce the communication cost compared with the general stochastic gradient descent federated learning.

scholarly journals Faster Distributed Deep Net Training: Computation and Communication Decoupled Stochastic Gradient Descent

2019 ◽  
Author(s):  
Shuheng Shen ◽  
Linli Xu ◽  
Jingchang Liu ◽  
Xianfeng Liang ◽  
Yifei Cheng
Keyword(s):  
Gradient Descent ◽  
Communication Complexity ◽  
Linear Time ◽  
Stochastic Gradient ◽  
Communication Cost ◽  
Stochastic Gradient Descent ◽  
Neural Network Training ◽  
Network Training ◽  
Successful Technique ◽  
Linear Iteration

With the increase in the amount of data and the expansion of model scale, distributed parallel training becomes an important and successful technique to address the optimization challenges. Nevertheless, although distributed stochastic gradient descent (SGD) algorithms can achieve a linear iteration speedup, they are limited significantly in practice by the communication cost, making it difficult to achieve a linear time speedup. In this paper, we propose a computation and communication decoupled stochastic gradient descent (CoCoD-SGD) algorithm to run computation and communication in parallel to reduce the communication cost. We prove that CoCoD-SGD has a linear iteration speedup with respect to the total computation capability of the hardware resources. In addition, it has a lower communication complexity and better time speedup comparing with traditional distributed SGD algorithms. Experiments on deep neural network training demonstrate the significant improvements of CoCoD-SGD: when training ResNet18 and VGG16 with 16 Geforce GTX 1080Ti GPUs, CoCoD-SGD is up to 2-3 x faster than traditional synchronous SGD.

scholarly journals Advanced Machine Learning model for Health Monitoring in IoT System

2021 ◽  
Vol 9 (10) ◽  
pp. 822-826
Author(s):  
Tanmayee Tushar Parbat
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Health Monitoring ◽  
Gradient Descent ◽  
Daily Basis ◽  
Stochastic Gradient Descent ◽  
Health Issues ◽  
Machine Learning Model ◽  
Cloud Server ◽  
Work Health

Abstract: Health issues are also concealed by a lack of health precautions on a daily basis. These issues frequently constitute a serious threat to public safety, which is frequently overlooked until it is too late. As a result, we have developed a set of principles to address and, to some extent, solve the issues outlined above. We continuously monitor the vital organs in our system; communicate data to cloud-based doctors, and alert patients to potential dangers. We designed an IoT system that connects several sensors to a microcomputer and sends collected data to a cloud server for Modified Stochastic Gradient Descent(SGD) Algorithm with a combination of deep learning. If the doctor suspects a health problem, he or she may issue a warning via our device after the examination is completed. Our proposed approach work Health Monitoring in IoT System Keywords: machine Learning, Health Monitoring, IoT System, Deep Learning

Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

2020 ◽  
Vol 4 (2) ◽  
pp. 329-335
Author(s):  
Rusydi Umar ◽  
Imam Riadi ◽  
Purwono
Keyword(s):  
Social Media ◽  
Gradient Descent ◽  
Classification Model ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Performance Patterns ◽  
A Company

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

2019 ◽  
Vol 12 (2) ◽  
pp. 120-127 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Gradient Descent ◽  
Autonomous Driving ◽  
Driving Behavior ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Safe Driving ◽  
Processing Pipeline ◽  
Self Driving Cars ◽  
And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

scholarly journals Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm

2021 ◽  
Author(s):  
Marco Mele ◽  
Cosimo Magazzino ◽  
Nicolas Schneider ◽  
Floriana Nicolai
Keyword(s):  
Economic Growth ◽  
Gradient Descent ◽  
Statistical Data ◽  
Stochastic Gradient Descent ◽  
Policy Recommendations ◽  
Dynamic Interactions ◽  
Gradient Descent Algorithm ◽  
The Relationship ◽  
Main Strand ◽  
Yearly Data

AbstractAlthough the literature on the relationship between economic growth and CO2 emissions is extensive, the use of machine learning (ML) tools remains seminal. In this paper, we assess this nexus for Italy using innovative algorithms, with yearly data for the 1960–2017 period. We develop three distinct models: the batch gradient descent (BGD), the stochastic gradient descent (SGD), and the multilayer perceptron (MLP). Despite the phase of low Italian economic growth, results reveal that CO2 emissions increased in the predicting model. Compared to the observed statistical data, the algorithm shows a correlation between low growth and higher CO2 increase, which contradicts the main strand of literature. Based on this outcome, adequate policy recommendations are provided.

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