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Course Outline
Introduction to Federated Learning in IoT and Edge Computing
- Overview of Federated Learning and its applications in IoT
- Key challenges in integrating Federated Learning with edge computing
- Benefits of decentralized AI in IoT environments
Federated Learning Techniques for IoT Devices
- Deploying Federated Learning models on IoT devices
- Handling non-IID data and limited computational resources
- Optimizing communication between IoT devices and central servers
Real-Time Decision-Making and Latency Reduction
- Enhancing real-time processing capabilities in edge environments
- Techniques for reducing latency in Federated Learning systems
- Implementing edge AI models for fast and reliable decision-making
Ensuring Data Privacy in Federated IoT Systems
- Data privacy techniques in decentralized AI models
- Managing data sharing and collaboration across IoT devices
- Compliance with data privacy regulations in IoT environments
Case Studies and Practical Applications
- Successful implementations of Federated Learning in IoT
- Practical exercises with real-world IoT datasets
- Exploring future trends in Federated Learning for IoT and edge computing
Summary and Next Steps
Requirements
- Experience in IoT or edge computing development
- Basic understanding of AI and machine learning
- Familiarity with distributed systems and network protocols
Audience
- IoT engineers
- Edge computing specialists
- AI developers
14 Hours
