Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Federated Learning
- Survey of Federated Learning concepts
- Decentralized model training compared to traditional centralized methods
- Advantages of Federated Learning regarding privacy and data security
Fundamental Federated Learning Algorithms
- Overview of Federated Averaging
- Building a simple Federated Learning model
- Evaluating Federated Learning against traditional machine learning techniques
Data Privacy and Security in Federated Learning
- Examining privacy concerns within AI
- Methods to improve privacy in Federated Learning
- Secure aggregation and data encryption strategies
Practical Implementation of Federated Learning
- Configuring a Federated Learning environment
- Constructing and training a Federated Learning model
- Implementing Federated Learning in real-world scenarios
Challenges and Limitations of Federated Learning
- Managing non-IID data in Federated Learning
- Addressing communication and synchronization hurdles
- Scaling Federated Learning for extensive networks
Case Studies and Future Trends
- Examples of successful Federated Learning deployments
- Investigating the future of Federated Learning
- New developments in privacy-preserving AI
Summary and Next Steps
Requirements
- Fundamental knowledge of machine learning concepts
- Proficiency in Python programming
- Knowledge of data privacy standards
Target Audience
- Data scientists
- Machine learning practitioners
- AI newcomers
14 Hours
