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Course Outline
Introduction to Federated Learning
- Comparison of traditional AI training versus federated learning.
- Core principles and advantages of federated learning.
- Use cases for federated learning in Edge AI applications.
Federated Learning Architecture and Workflow
- Exploration of client-server and peer-to-peer federated learning models.
- Data partitioning and decentralized model training methods.
- Communication protocols and aggregation strategies.
Implementing Federated Learning with TensorFlow Federated
- Configuration of TensorFlow Federated for distributed AI training.
- Construction of federated learning models using Python.
- Simulation of federated learning on edge devices.
Federated Learning with PyTorch and OpenFL
- Introduction to OpenFL for federated learning.
- Implementation of PyTorch-based federated models.
- Customization of federated aggregation techniques.
Optimizing Performance for Edge AI
- Hardware acceleration techniques for federated learning.
- Strategies for reducing communication overhead and latency.
- Adaptive learning approaches for resource-constrained devices.
Data Privacy and Security in Federated Learning
- Privacy-preserving techniques, including Secure Aggregation, Differential Privacy, and Homomorphic Encryption.
- Mitigation of data leakage risks in federated AI models.
- Regulatory compliance and ethical considerations.
Deploying Federated Learning Systems
- Setup of federated learning on actual edge devices.
- Monitoring and updating of federated models.
- Scaling federated learning deployments within enterprise environments.
Future Trends and Case Studies
- Emerging research 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 concepts.
- Practical experience with Python programming and AI frameworks such as PyTorch, TensorFlow, or comparable tools.
- Foundational knowledge of distributed computing and networking principles.
- Familiarity with data privacy and security concepts relevant to AI.
Target Audience
- AI researchers
- Data scientists
- Security specialists
21 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
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