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Course Outline
Introduction to Federated Learning
- Comparing 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
- Understanding client-server and peer-to-peer federated learning architectures.
- Data partitioning and decentralized model training methods.
- Communication protocols and aggregation strategies.
Implementing Federated Learning with TensorFlow Federated
- Configuring TensorFlow Federated for distributed AI training.
- Constructing federated learning models using Python.
- Simulating federated learning environments on edge devices.
Federated Learning with PyTorch and OpenFL
- Overview of OpenFL for federated learning.
- Developing PyTorch-based federated models.
- Tailoring federated aggregation techniques.
Optimizing Performance for Edge AI
- Leveraging hardware acceleration for federated learning.
- Minimizing communication overhead and latency.
- Adaptive learning strategies tailored 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 infrastructure on real edge devices.
- Monitoring and updating federated models.
- Scaling federated learning deployments within enterprise environments.
Future Trends and Case Studies
- Emerging research developments in federated learning and Edge AI.
- Real-world case studies from healthcare, finance, and IoT sectors.
- Strategic steps for advancing federated learning solutions.
Summary and Next Steps
Requirements
- A solid understanding of machine learning and deep learning concepts.
- Proficiency in Python programming and experience with AI frameworks such as PyTorch, TensorFlow, or similar tools.
- Foundational knowledge of distributed computing and networking.
- Familiarity with data privacy and security principles in the context of AI.
Target Audience
- AI researchers.
- Data scientists.
- Security specialists.
21 Hours
Testimonials (1)
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