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Duration 14 hours
Course Outline
Overview of Federated Learning
- Core concepts of Federated Learning
- Contrasting decentralized model training with traditional centralized methods
- Advantages of Federated Learning regarding privacy and data security
Foundational Federated Learning Algorithms
- Introduction to Federated Averaging
- Building a simple Federated Learning model
- Evaluating Federated Learning against standard machine learning approaches
Privacy and Security in Federated Learning
- Identifying data privacy challenges in AI
- Strategies to bolster privacy within Federated Learning
- Methods for secure aggregation and data encryption
Applying Federated Learning in Practice
- Configuring a Federated Learning environment
- Developing and training Federated Learning models
- Implementing Federated Learning in real-world contexts
Obstacles and Constraints of Federated Learning
- Managing non-IID data in Federated Learning setups
- Resolving communication and synchronization difficulties
- Scaling Federated Learning for extensive networks
Case Studies and Emerging Trends
- Analysis of successful Federated Learning applications
- Forecasting the trajectory of Federated Learning
- Exploring new trends in privacy-preserving AI
Conclusion and Future Pathways
Requirements
- A foundational grasp of machine learning principles
- Proficiency in Python programming
- Awareness of data privacy standards
Target Audience
- Data scientists
- Machine learning practitioners
- Novices in AI
