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Federated Learning

Personalized Diagnosis of Symptoms you own this product

This project is part of the liveProject series Federated Learning Over Networks for Pandemics
prerequisites
intermediate Python • basics of data science • basics of machine learning • basics of NetworkX
skills learned
use NetworkX to jointly represent network structured data and models • learn the weights of a linear classifier using gradient descent • implement a Federated Learning algorithm by combining gradient descent with a simple averaging scheme
Alexander Jung
1 week · 8-10 hours per week · ADVANCED
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liveProject This project is part of the liveProject series Federated Learning Over Networks for Pandemics liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
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In this liveProject, you’ll apply the federated learning machine learning paradigm to compute a personalized infection risk diagnosis for COVID-19. Federated learning is an ML paradigm that learns from decentralized data via distributed computing environments. The risk diagnosis will come from a tailored classifier that is trained separately for each individual, by combining gradient descent with a network averaging method. You’ll then develop a federate learning algorithm that can be implemented by scalable passing messages over the contact network.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Alexander Jung
Alexander Jung is an assistant professor for machine learning at Aalto University in Finland. Prior to joining Aalto, he obtained a PhD in statistical signal processing from TU Vienna in 2012 and was a postdoc at TU Vienna and ETH Zurich. Alex leads the Aalto research group “Machine Learning for Big Data'' that studies the fundamental limits and efficient algorithms for machine learning from large distributed collections of data. His current research focus is on privacy preserving and explainable federated machine learning methods for big data over networks. Alex has developed some of the most popular courses at Aalto University. He was selected as the Teacher of the Year by the Department of Computer Science in 2018.

prerequisites

This liveProject is for Python data scientists interested in applying big data analytics to public healthcare. To begin this liveProject you will need to be familiar with the following:

TOOLS
  • Intermediate Python (declaring variables, loops, branches, debugging, importing modules)
  • Basics of Matplotlib
  • Basics of NumPy
  • Basics of GeoPy
  • Basics of NetworkX
TECHNIQUES
  • Basics of data science (logistic regression, gradient descent, using networks, distributed algorithms to compute gradients)

you will learn

In this liveProject, you’ll explore federated learning algorithms by combining gradient descent for logistic regression with network averaging methods. These algorithms can be used to create personalized predictions for individuals.

  • Use NetworkX to jointly represent network structured data and models
  • Learn the weights of a linear classifier using gradient descent
  • Implement a Federated Learning algorithm by combining gradient descent with a simple averaging scheme

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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