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Course Outline
Introduction to Federated Learning <\/p>
- Comparison of traditional AI training with federated learning <\/li>
- Fundamental principles and benefits of federated learning <\/li>
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Applications of federated learning in Edge AI scenarios
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Federated Learning Architecture and Workflow <\/p>
- Exploring client-server and peer-to-peer federated learning models <\/li>
- Data partitioning and decentralized model training <\/li>
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Communication protocols and aggregation strategies
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Implementing Federated Learning with TensorFlow Federated <\/p>
- Configuring TensorFlow Federated for distributed AI training <\/li>
- Constructing federated learning models using Python <\/li>
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Simulating federated learning on edge devices
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Federated Learning with PyTorch and OpenFL <\/p>
- Overview of OpenFL for federated learning <\/li>
- Developing PyTorch-based federated models <\/li>
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Tailoring federated aggregation techniques
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Optimizing Performance for Edge AI <\/p>
- Hardware acceleration for federated learning <\/li>
- Minimizing communication overhead and latency <\/li>
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Adaptive learning strategies for resource-constrained devices
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Data Privacy and Security in Federated Learning <\/p>
- Privacy-preserving techniques (Secure Aggregation, Differential Privacy, Homomorphic Encryption) <\/li>
- Reducing data leakage risks in federated AI models <\/li>
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Regulatory compliance and ethical considerations
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Deploying Federated Learning Systems <\/p>
- Establishing federated learning on real edge devices <\/li>
- Monitoring and updating federated models <\/li>
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Scaling federated learning deployments in enterprise environments
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Future Trends and Case Studies <\/p>
- Emerging research in federated learning and Edge AI <\/li>
- Real-world case studies in healthcare, finance, and IoT <\/li>
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Next steps for advancing federated learning solutions
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Summary and Next Steps <\/p>
Requirements
- Solid understanding of machine learning and deep learning concepts <\/li>
- Proficiency in Python programming and AI frameworks (such as PyTorch, TensorFlow, or similar tools) <\/li>
- Fundamental knowledge of distributed computing and networking <\/li>
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Familiarity with data privacy and security principles in AI
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Target Audience<\/strong> <\/p>
- AI researchers <\/li>
- Data scientists <\/li>
- Security specialists <\/li> <\/ul>
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
