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Course Outline
Introduction to Federated Learning
- Comparison between traditional AI training and federated learning.
- Core principles and advantages of federated learning.
- Use cases of federated learning in Edge AI applications.
Federated Learning Architecture and Workflow
- Exploring client-server and peer-to-peer federated learning models.
- Data partitioning and decentralized model training.
- Communication protocols and aggregation strategies.
Implementing Federated Learning with TensorFlow Federated
- Configuring TensorFlow Federated for distributed AI training.
- Developing federated learning models using Python.
- Simulating federated learning on edge devices.
Federated Learning with PyTorch and OpenFL
- Overview of OpenFL for federated learning.
- Implementing PyTorch-based federated models.
- Customizing federated aggregation techniques.
Optimizing Performance for Edge AI
- Hardware acceleration for federated learning.
- Minimizing communication overhead and latency.
- Adaptive learning strategies for resource-constrained devices.
Data Privacy and Security in Federated Learning
- Privacy-preserving techniques (Secure Aggregation, Differential Privacy, Homomorphic Encryption).
- Mitigating data leakage risks in federated AI models.
- Regulatory compliance and ethical considerations.
Deploying Federated Learning Systems
- Setting up federated learning on real edge devices.
- Monitoring and updating federated models.
- Scaling federated learning deployments in enterprise environments.
Future Trends and Case Studies
- Emerging research in federated learning and Edge AI.
- Real-world case studies in healthcare, finance, and IoT.
- Next steps for advancing federated learning solutions.
Summary and Next Steps
Requirements
- Robust understanding of machine learning and deep learning concepts.
- Proficiency in Python programming and AI frameworks such as PyTorch, TensorFlow, or similar tools.
- Fundamental knowledge of distributed computing and networking.
- Familiarity with data privacy and security principles in 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
