Federated Learning and Privacy Preserving AI Course

Training Outlines

Introduction

Federated Learning and Privacy Preserving AI represent the forefront of secure and ethical artificial intelligence development. This 5-day professional course is designed to equip participants with a deep understanding of federated learning principles and the methodologies used to enhance privacy in AI systems. Throughout this course, attendees will gain hands-on experience with real-world applications and learn how to integrate these compelling technologies effectively.

• Understand the fundamentals of federated learning and its advantages over traditional approaches.
• Gain insights into various privacy-preserving techniques employed in artificial intelligence.
• Explore real-world case studies and applications of federated learning.
• Learn how to implement privacy-preserving protocols in AI systems.
• Develop skills to address challenges in the deployment of secure AI models.

Course Outlines

Day 1: Introduction to Federated Learning and Privacy Principles

• Overview of federated learning: Concepts and significance
• Comparison with centralized and decentralized learning methods
• Basic privacy principles in AI and their importance
• Challenges and limitations in traditional AI models concerning privacy
• Introductory case studies highlighting federated learning applications

Day 2: Core Techniques in Federated Learning

• Data partitioning and model aggregation methodologies
• Understanding federated averaging and optimization techniques
• Communication strategies between federated nodes
• Overcoming challenges in data heterogeneity
• Hands-on session: Setting up a basic federated learning system

Day 3: Privacy-Preserving Techniques and Algorithms

• Differential privacy and its application in AI
• Secure multi-party computation and homomorphic encryption
• Challenges in balancing privacy, utility, and efficiency
• Exploring adversarial attacks and defenses in federated settings
• Practical exercises on implementing privacy algorithms

Day 4: Real-world Applications and Case Studies

• Federated learning in healthcare: Opportunities and case studies
• Implementation in autonomous vehicles and IoT
• Federal banking and finance applications
• Exploration of the use of federated learning in social media and advertising
• Enterprise-level data privacy case studies

Day 5: Deployment Challenges and Future Directions

• Scalability and infrastructure considerations
• Legal and ethical considerations in privacy-preserving AI
• Future trends and enhancements in federated learning
• Strategizing the implementation of federated learning across industries
• Final project: Designing a federated learning model with privacy measures

Training Schedule

Below is the table of cities along with the respective dates for the upcoming training sessions of Federated Learning and Privacy Preserving AI Course. Please review the schedule to find the most convenient option for you. You can also use the below search bar to type the city name and filter the results.