Federated Learning Seminar - #127

Overview

Join us for the Federated Learning Seminar - #127, where you'll explore cutting-edge concepts in federated learning presented by Alexander Tyurin from Skoltech and AIRI. This seminar is part of the FLOW series, dedicated to unpacking the complexities and innovations of distributed machine learning.

Ideal for enthusiasts and professionals eager to dive into the advanced research and applications shaping the future of artificial intelligence.

Syllabus

• Course Introduction
Overview of Federated Learning
Objectives of the Seminar
• Foundations of Federated Learning
Definitions and Key Concepts
Differences from Traditional Machine Learning
• Architecture of Federated Learning Systems
Client-Server Model
Communication Protocols
• Privacy and Security in Federated Learning
Data Privacy Concerns
Techniques for Enhancing Security
• Advanced Research Topics
Personalization and Heterogeneity
Federated Learning Optimization Algorithms
• Applications of Federated Learning
Healthcare
Finance
Internet of Things (IoT) Devices
• Case Study Analysis
Notable Federated Learning Projects and Outcomes
• Challenges and Future Directions
Scalability Issues
Regulatory and Ethical Considerations
• Hands-On Session
Setting up a Federated Learning Experiment
Analyzing Results and Modifying Parameters
• Guest Lecture by Alexander Tyurin
Insights from Research at Skoltech and AIRI
Q&A Session
• Closing Discussion
Recap of Seminar Topics
Open Forum for Participant Questions
• Resources and Further Reading
Recommended Books, Papers, and Online Materials

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Subjects

Computer Science