Federated Learning

Dive into the world of Federated Learning (FL) with this comprehensive course! You'll gain a deep understanding of this revolutionary machine learning approach, learning to build and deploy models while preserving data privacy.

First, we'll lay the groundwork by exploring the core concepts of Neural Networks (NN) and how they function. Then, get hands-on experience by implementing a Neural Network from scratch using PyTorch.

Next, we'll take a quick look at the Federated Learning architecture before diving into the practical aspects of data handling. You'll learn how to load datasets on devices in various scenarios: IID (Independent and Identically Distributed), non-IID, and unbalanced datasets. We'll also cover a PySyft tutorial demonstrating how to securely send and receive models and datasets between clients and a server.

This course focuses on mastering Federated Learning by meticulously examining the techniques and algorithms presented in the original research papers. We'll go beyond theory and implement key FL algorithms line by line, including:

• FedAvg (Federated Averaging)
• FedSGD (Federated Stochastic Gradient Descent)
• FedProx (Federated Proximal)
• FedDANE (Federated Denoising Autoencoder)

You'll also discover Differential Privacy (DP) and learn how to integrate it into your Federated Learning workflows. We'll then implement FedAvg with Differential Privacy to ensure robust data protection.

This course equips you with the skills to implement FL techniques both locally and on the cloud. For cloud deployments, we'll leverage Google Cloud Platform (GCP) to set up and configure the necessary instances for your experiments.

By the end of this course, you'll be empowered to:

• Implement diverse Federated Learning techniques.
• Build your own custom optimizers and techniques.
• Run experiments effectively in both local and cloud environments.

Whether you're a machine learning enthusiast or a data scientist looking to enhance your skillset, this course provides a practical and in-depth exploration of Federated Learning.