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Course Outline
Introduction to Federated Learning
- Overview of core Federated Learning concepts
- Contrasting decentralized model training with traditional centralized methods
- Exploring the privacy and data security advantages of Federated Learning
Basic Federated Learning Algorithms
- Introduction to Federated Averaging
- Building a simple Federated Learning model
- Comparing Federated Learning with standard machine learning approaches
Data Privacy and Security in Federated Learning
- Analyzing data privacy concerns in artificial intelligence
- Strategies for strengthening privacy within Federated Learning
- Methods for secure aggregation and data encryption
Practical Implementation of Federated Learning
- Configuring a Federated Learning environment
- Developing and training Federated Learning models
- Deploying Federated Learning in real-world contexts
Challenges and Limitations of Federated Learning
- Managing non-IID data in Federated Learning setups
- Navigating communication and synchronization complexities
- Scaling Federated Learning across extensive networks
Case Studies and Future Trends
- Examining successful Federated Learning implementations
- Looking ahead to the future of Federated Learning
- Identifying emerging trends in privacy-preserving AI
Summary and Next Steps
Requirements
- Foundational knowledge of machine learning concepts
- Proficiency in Python programming
- Awareness of data privacy standards
Intended Audience
- Data scientists
- Machine learning enthusiasts
- Aspiring AI professionals
14 Hours
