Federated Learning Theory And Practical
Federated Learning: Theory And Practical
Published 10/2024
Created by Amir Anees
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 41 Lectures ( 4h 23m ) | Size: 1.67 GB
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.
Who this course is for
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.
https://rapidgator.net/file/74528bc02e8561d4068f793c1fd84932/Federated_Learning_Theory_and_Practical.part2.rar.html
https://rapidgator.net/file/797a67e1ba113ff5112f87149a558f93/Federated_Learning_Theory_and_Practical.part1.rar.html
