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.
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:
- Intermediate Python (declaring variables, loops, branches, debugging, importing modules)
- Basics of Matplotlib
- Basics of NumPy
- Basics of GeoPy
- Basics of NetworkX
- 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