Introduction to Federated Learning

• Basic understanding of machine learning concepts
• Experience with Python programming
• Familiarity with data privacy principles

Audience

• Data scientists
• Machine learning enthusiasts
• AI beginners

Federated Learning is a decentralized approach to training machine learning models across multiple devices or servers without sharing raw data. This course provides a foundational understanding of Federated Learning, covering basic concepts, benefits, and introductory techniques for implementing decentralized machine learning models.

This instructor-led, live training (online or onsite) is aimed at beginner-level professionals who wish to learn the fundamentals of Federated Learning and its practical applications.

By the end of this training, participants will be able to:

• Understand the principles of Federated Learning.
• Implement basic Federated Learning algorithms.
• Address data privacy concerns using Federated Learning.
• Integrate Federated Learning into existing AI workflows.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Federated Learning

• Overview of Federated Learning concepts
• Decentralized model training vs. traditional centralized approaches
• Benefits of Federated Learning in privacy and data security

Basic Federated Learning Algorithms

• Introduction to Federated Averaging
• Implementation of a simple Federated Learning model
• Comparison of Federated Learning with traditional machine learning

Data Privacy and Security in Federated Learning

• Understanding data privacy concerns in AI
• Techniques for enhancing privacy in Federated Learning
• Secure aggregation and data encryption methods

Practical Implementation of Federated Learning

• Setting up a Federated Learning environment
• Building and training a Federated Learning model
• Deploying Federated Learning in real-world scenarios

Challenges and Limitations of Federated Learning

• Handling non-IID data in Federated Learning
• Communication and synchronization issues
• Scaling Federated Learning for large networks

Case Studies and Future Trends

• Case studies of successful Federated Learning implementations
• Exploring the future of Federated Learning
• Emerging trends in privacy-preserving AI

Summary and Next Steps