Abstract
Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model with training data distributed over a large number of clients each with unreliable and relatively slow network connections.
Description
Federated learning is a family of Machine Learning algorithms that has the core idea: a connected network exists in which there is a central server node. Each of the nodes creates data – that has to be used for training as well as for prediction. Each of the nodes trains a local model and only that model is shared with the server, not the data. In this talk, We talk about how to build deep learning models using federated learning that is truly privacy-preserving. We will show how to build custom algorithms and loss functions.
Key Takeaways:
- Introduction to Federated Learning
- Decentralized Training
- Encryption
- Differential Privacy
- Federated Learning – Notebook
- Introduction
- Custom algorithm and loss function
Presentation Video
Coming soon!!
Tuhin Sharma
Senior Principal Data Scientist
My research interests include AI, NLP and Distributed Computing.