A Brief Introduction to Federated Learning

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The need to protect privacy

The need for privacy has never been more immediate than now — a time where the value of data is likened to oil and considered to be an individual’s most important asset. The types of data that individuals generate range from seemingly harmless details such as retail preferences to highly sensitive information like medical records, and even political views which can and have affected a national election [1].

One possible answer — that is getting more and more traction — to this call for increased data security is the development of privacy-preserving machine learning — also known as Federated Learning. At a high level, Federated Learning is a machine learning approach that trains models collaboratively and aims to prevent data leakage by using differential privacy and decentralized data. …

I wrote this because I couldn’t find many tutorials showing the detailed math for this calculation. So I decided to write it out thoroughly and show all the steps in case people who also haven't done calculus in a while need a little direction.

A Gaussian distribution is easily recognizable by a cluster of data centered around the mean and a symmetrical distribution of points around the mean. …

Analyzing bank churn data using logistic regression, variance inflation factors, and odds ratios

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What is Gretl?

Gretl is an open source statistics package that was developed for econometrics but can be used for a wide variety of data. The program is written in C++ and has many community sourced packages that extend the core program.

The program can be downloaded here and the data used in this tutorial can found on Kaggle here.

Taking a look at the data

The data can be loaded into Gretl from File and selecting the downloaded csv. …


Gurkamal Deol

I’m a bioinformatics masters student and software developer. I’m Interested in ML, statistics, & biology. LinkedIn: www.linkedin.com/in/gurkamal-deol-508675174

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