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Course Outline
Introduction to Federated Learning
- Comparison of traditional AI training with federated learning.
- Core principles and benefits of federated learning.
- Applications of federated learning in Edge AI scenarios.
Federated Learning Architecture and Workflow
- Exploring client-server and peer-to-peer federated learning models.
- Data partitioning and decentralized model training processes.
- Communication protocols and aggregation strategies.
Implementing Federated Learning with TensorFlow Federated
- Configuring TensorFlow Federated for distributed AI training.
- Building federated learning models using Python.
- Simulating federated learning scenarios on edge devices.
Federated Learning with PyTorch and OpenFL
- Introduction to OpenFL for federated learning.
- Developing PyTorch-based federated models.
- Customizing federated aggregation techniques.
Optimizing Performance for Edge AI
- Leveraging hardware acceleration for federated learning.
- Minimizing communication overhead and latency.
- Implementing adaptive learning strategies for devices with limited resources.
Data Privacy and Security in Federated Learning
- Privacy-preserving methods including Secure Aggregation, Differential Privacy, and Homomorphic Encryption.
- Reducing risks of data leakage in federated AI models.
- Navigating regulatory compliance and ethical considerations.
Deploying Federated Learning Systems
- Establishing federated learning on actual edge devices.
- Monitoring and updating federated models.
- Scaling federated learning deployments within enterprise environments.
Future Trends and Case Studies
- Latest research developments in federated learning and Edge AI.
- Real-world case studies from healthcare, finance, and IoT sectors.
- Next steps for advancing federated learning solutions.
Summary and Next Steps
Requirements
- A solid grasp of machine learning and deep learning concepts.
- Proficiency in Python programming along with AI frameworks such as PyTorch, TensorFlow, or comparable tools.
- Foundational knowledge of distributed computing and networking.
- Awareness of data privacy and security principles within AI.
Target Audience
- AI researchers.
- Data scientists.
- Security specialists.
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
