Course Code: fliot
Duration: 14 hours
Prerequisites:
  • 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
Overview:

Federated Learning is enabling decentralized AI model training directly on IoT devices and edge computing platforms. This course explores the integration of Federated Learning into IoT and edge environments, focusing on reducing latency, enhancing real-time decision-making, and ensuring data privacy in distributed systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to apply Federated Learning to optimize IoT and edge computing solutions.

By the end of this training, participants will be able to:

  • Understand the principles and benefits of Federated Learning in IoT and edge computing.
  • Implement Federated Learning models on IoT devices for decentralized AI processing.
  • Reduce latency and improve real-time decision-making in edge computing environments.
  • Address challenges related to data privacy and network constraints in IoT systems.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • 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 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

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