Federated Learning: Theory And Practical

An Introduction to Federated Learning: Concepts, Implementation, and Privacy Considerations

What you'll learn

Learn the fundamentals and architecture of federated learning

Differentiate between various types of federated learning approaches

Apply federated learning in practical scenarios and combined frameworks

Understand the privacy, security, and communication aspects of federated learning

Requirements

Basic understanding of machine learning concepts and algorithms. Familiarity with Python programming and popular ML libraries (e.g., TensorFlow, PyTorch). No prior knowledge of federated learning is required—this course will cover the essentials.

Description

"Federated Learning: Theory and Practical" is designed to provide you with a comprehensive introduction to one of the most exciting and evolving areas in machine learning—federated learning (FL). In an era where data privacy is becoming increasingly important, FL offers a solution by enabling machine learning models to be trained across decentralized data sources, such as smartphones or local clients, without the need to share sensitive data.This course starts with the basics of machine learning to ensure a solid foundation. You will then dive into the core concepts of federated learning, including the motivations behind its development, the different types (horizontal, vertical, and combined FL), and how it compares to traditional machine learning approaches.By week three, you'll not only grasp the theory but also be ready to implement FL systems from scratch and using popular frameworks like FLOWER. You’ll explore advanced topics such as privacy-enhancing technologies, including differential privacy and homomorphic encryption, and gain insight into practical challenges like client selection and gradient inversion attacks.Whether you are a data scientist, machine learning engineer, or someone curious about privacy-preserving AI, this course offers the theoretical grounding and hands-on skills necessary to navigate the emerging landscape of federated learning.

Overview

Section 1: Week 0: Course Introduction

Lecture 1 Course Intro

Section 2: Week 1: ML Basics

Lecture 2 Machine Learning

Lecture 3 Neural Network Architecture

Lecture 4 NN Model Parameters

Lecture 5 NN Training

Lecture 6 NN Forward Propagation

Lecture 7 NN Loss Computation

Lecture 8 NN Gradient Descent

Lecture 9 NN Backward Propagation

Lecture 10 NN Recap

Lecture 11 NN Scratch Implementation

Lecture 12 NN PyTorch Implementation

Section 3: Week 2: FL Basics

Lecture 13 FL Motivations

Lecture 14 FL Intro

Lecture 15 FL Implementation

Lecture 16 FL Scratch Implementation

Lecture 17 FL FLOWER Implementation

Lecture 18 FL FedAllImplementation

Section 4: Week 3: FL Types

Lecture 19 FL Types Intro

Lecture 20 Horizontal FL

Lecture 21 Vertical FL Intro

Lecture 22 Vertical FL Theory

Lecture 23 Vertical FL Theory Implementation

Lecture 24 Vertical FL Scratch Implementation

Lecture 25 Vertical FL FedAll Implementation

Section 5: Week 4: Combined FL

Lecture 26 FL Combined Scenario 1-Intro

Lecture 27 FL Combined Scenario 1-Theory

Lecture 28 FL Combined Scenario 1-Impl Intro

Lecture 29 FL Combined Scenario 1-Implementation

Lecture 30 FL Combined Scenario 2-Theory

Lecture 31 FL Combined Scenario 2-Impl Intro

Lecture 32 FL Combined Scenario 2-Implementation

Section 6: Week 5: Other Topics in FL

Lecture 33 FL Performance

Lecture 34 FL Performance-Combined

Lecture 35 FL Time

Lecture 36 FL Privacy

Lecture 37 FL Differential Privacy

Lecture 38 FL Homomorphic Encryption

Lecture 39 FL Homomorphic Encryption Implementation

Lecture 40 FL Client Selection

Lecture 41 FL Other Topics

This course is designed for data scientists, machine learning engineers, and AI enthusiasts who want to deepen their understanding of federated learning. It’s also ideal for professionals looking to apply privacy-preserving machine learning techniques in distributed environments. Whether you're familiar with machine learning or new to federated learning, this course offers valuable insights for those interested in practical implementation of FL models.