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Course Outline
Introduction to Federated Learning
- What is federated learning, and how does it differ from centralized learning?
- Advantages of federated learning for secure AI collaboration in sensitive sectors.
- Use cases and applications in industries with stringent data protection requirements.
Core Components of Federated Learning
- Federated data, client participation, and model aggregation techniques.
- Communication protocols and update mechanisms for efficient collaboration.
- Strategies for managing heterogeneity in federated environments to ensure robust performance.
Data Privacy and Security in Federated Learning
- Principles of data minimization and privacy by design in federated learning.
- Techniques for securing model updates, including differential privacy and other advanced methods.
- Compliance with data protection regulations to ensure legal and ethical standards are met.
Implementing Federated Learning
- Steps for setting up a federated learning environment, including infrastructure and software requirements.
- Distributed model training using federated frameworks to enhance collaboration and efficiency.
- Considerations for performance and accuracy in federated learning models.
Federated Learning in Healthcare
- Addressing secure data sharing and privacy concerns specific to the healthcare industry.
- Collaborative AI applications for medical research, diagnosis, and patient care.
- Case studies demonstrating the use of federated learning in medical imaging and diagnostic tools.
Federated Learning in Finance
- Utilizing federated learning for secure financial modeling and data analysis.
- Implementing federated approaches for fraud detection and risk assessment.
- Case studies highlighting secure data collaboration within financial institutions to enhance decision-making processes.
Challenges and Future of Federated Learning
- Technical and operational challenges in deploying federated learning for government and industry applications.
- Emerging trends and advancements in federated AI technologies.
- Exploring new opportunities for federated learning across various sectors to drive innovation and improve data security.
Summary and Next Steps
Requirements
- Fundamental knowledge of machine learning principles
- Understanding of data privacy and security basics
Audience
- Data scientists and AI researchers specializing in privacy-preserving machine learning for government and other sectors
- Healthcare and finance professionals managing sensitive information
- IT and compliance managers seeking secure AI collaboration methods for government and industry
14 Hours
