Federated Learning and Edge AI Training Course
Federated learning is a decentralized approach to AI training that allows edge devices to collaboratively train models without sharing raw data, thereby enhancing both privacy and efficiency.
This instructor-led, live training (available online or onsite) is designed for advanced-level AI researchers, data scientists, and security specialists who want to implement federated learning techniques to train AI models across multiple edge devices while maintaining data privacy.
By the end of this training, participants will be able to:
- Understand the principles and advantages of federated learning in Edge AI.
- Implement federated learning models using TensorFlow Federated and PyTorch.
- Optimize AI training processes across distributed edge devices.
- Tackle data privacy and security challenges in federated learning.
- Deploy and monitor federated learning systems in real-world scenarios.
Format of the Course
- Interactive lectures and discussions.
- Extensive exercises and practical activities.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction to Federated Learning
- Overview of traditional AI training vs. federated learning
- Key principles and advantages of federated learning
- Use cases of federated learning in Edge AI applications
Federated Learning Architecture and Workflow
- Understanding 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
- Setting up TensorFlow Federated for distributed AI training
- Building federated learning models using Python
- Simulating federated learning on edge devices
Federated Learning with PyTorch and OpenFL
- Introduction to OpenFL for federated learning
- Implementing PyTorch-based federated models
- Customizing federated aggregation techniques
Optimizing Performance for Edge AI
- Hardware acceleration for federated learning
- Reducing 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
- Strong understanding of machine learning and deep learning concepts
- Experience with Python programming and AI frameworks (PyTorch, TensorFlow, or similar)
- Basic knowledge of distributed computing and networking
- Familiarity with data privacy and security concepts in AI
Audience
- AI researchers
- Data scientists
- Security specialists
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