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Course Outline
Introduction to Federated Learning
- Comparison of traditional AI training methods with federated learning
- Core principles and benefits of federated learning
- Practical applications of federated learning in Edge AI
Federated Learning Architecture and Workflow
- Exploring client-server and peer-to-peer federated learning models
- Data partitioning and decentralized model training techniques
- Communication protocols and model 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
- Building 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 resource-constrained devices
Data Privacy and Security in Federated Learning
- Privacy-preserving techniques (Secure Aggregation, Differential Privacy, Homomorphic Encryption)
- Mitigating risks of data leakage in federated AI models
- 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 settings
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 robust understanding of machine learning and deep learning principles
- 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 concepts 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
