Learn Julia: Privacy Protection with Contexts and Capabilities

Learn Julia: Privacy Protection with Contexts and Capabilities

In this Julia tutorial, you'll learn how to use contexts and capabilities to provide privacy protection

Privacy is an important aspect of the internet today. Providing privacy protection, however, is a difficult problem especially when you work with many data processes and systems. To solve this problem holistically, privacy needs to be a built-in feature, not an after-thought. I will talk about how to solve this problem with the idea of context and capabilities.

#julia 

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Preserving Data Privacy in Deep Learning | Part 1

Understanding the basics of Federated Learning and its implementation using PyTorch

Many thanks to renowned data scientist Mr. Akshay Kulkarni for his inspiration and guidance on this tutorial.

In the world revolutionized by data and digitalization, more and more personal information is shared and stored, which opens a new field of preserving data privacy. But, what is data privacy, and why is there a need for preserving it?

Data Privacy defines how a particular piece of information/data should be handled or who has authorized access based on its relative importance. With the introduction to AI (Machine Learning and Deep Learning), a lot of personal information can be extracted from these models, which can cause irreparable damage to the people whose personal data has been exposed. So, here comes the need to preserve this data while implementing various machine learning models.

In this series of tutorials, the major concern is to preserve the data-privacy in Deep Learning models. You will be exploring different methods like Federated Learning, Differential Privacy, and Homomorphic Encryption.

In this tutorial, you will discover how to preserve data privacy using federated learning on machine learning models. After completing this tutorial, you will know:

1. The basics of Federated Learning
2. Dividing data amongst the clients (for federated learning)
3. The model architecture
4. Aggregation of the Decentralized Weights in Global Weights

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Introduction

F

ederated Learning, also known as collaborative learning, is a deep learning technique where the training takes place across multiple decentralized edge devices (clients) or servers on their personal data, without sharing the data with other clients, thus keeping the data private. It aims at training a machine learning algorithm, say, deep neural networks on multiple devices (clients) having local datasets without explicitly exchanging the data samples.

This training happens simultaneously on other devices, hundreds, and thousands of them. After training the same model on different devices with different data, their weights (summary of training) are sent to the global server, where aggregation of these weights takes place. Different aggregation techniques are used to get the most out of the weights learned on the clients’ devices. After aggregation, the global weights are again sent to the clients, and the training continues on the client’s device. This entire process is called a communication round in federated learning, and this is how many communication rounds take place to further improve the accuracy of the model.

#deep-learning #federated-learning #privacy #differential-privacy #deep learning

Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

Learn Julia: Privacy Protection with Contexts and Capabilities

Learn Julia: Privacy Protection with Contexts and Capabilities

In this Julia tutorial, you'll learn how to use contexts and capabilities to provide privacy protection

Privacy is an important aspect of the internet today. Providing privacy protection, however, is a difficult problem especially when you work with many data processes and systems. To solve this problem holistically, privacy needs to be a built-in feature, not an after-thought. I will talk about how to solve this problem with the idea of context and capabilities.

#julia 

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

Visit Blog- https://www.xplace.com/article/8743

#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert

Preserving Data Privacy in Deep Learning | Part 2

Many thanks to renowned data scientist for his inspiration and guidance on this tutorial.

The above image resembles the Non-IID (Independent and Identically Distributed) dataset. A collection of random variables (images in our case) is independent and identically distributed if each random variable (image) has a similar probability distribution as the others, and all are mutually independent. In part 1 of this series, we used the CIFAR10 dataset, an example of an IID type, but for the real-world use case, there needs to be a non-IID dataset to represent the real-world scenario. So, What is non-IID data? What changes to make in the current dataset (CIFAR10) to accumulate non-IID data for Federated Learning?

These are some of the questions which will be answered in this tutorial. This blog is part 2 of the series Preserving Data Privacy in Deep Learning and focuses on the distribution of the CIFAR10 into a non-IID dataset further divided among the clients. After completing this tutorial, you will know:

1. Non-IID Dataset
2. Conversion of a balanced dataset into the non-IID and real-world datasets
3. Forming clients encapsulating a part of this non-IID/real-world dataset
4. Use cases of image classification using federated learning

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An analogy to the non-IID dataset. Photo by Harsh Yadav

Real-life data (referring to objects, values, attributes, and other aspects) is essentially non-independent and identically distributed (non-IID). In contrast, most of the existing analytical or machine learning methods are based on IID data. So, there needs to be a proper approach to handle such type of real-world dataset. This tutorial will lead to a non-IID dataset’s foundations and thus open the stage for implementing various federated learning techniques to handle the problem of getting insights from non-IID data. Non-IIDness is a common problem, causing unstable performances of deep learning models. In literature, the non-IID image classification problem is largely understudied.

NICO (Non-IID Image dataset with contexts) is one such benchmark dataset that can be further used to develop state-of-the-art machine learning models to tackle non-IID data.

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In this series, CIFAR 10 is used as the benchmark dataset, and further, it is converted into a non-IID dataset. To learn more about the basics of federated learning, please head over to part 1 of this series. In this tutorial, we will create two different types of the dataset, one is replicating the real-life data, i.e. **real-world dataset, **and another one is the extreme example of a non-iid dataset.

**REAL-WORLD DATASET: **CIFAR 10 is randomly divided into the given number of clients. So, a client can have images from any number of classes, say, one client has images from only 1 class and another client has images from 5 classes. This type of dataset replicates the real-world scenario where clients can have different types of images.

#deep-learning #federated-learning #non-iid #privacy #pytorch #deep learning