Course Outline

Introduction to Federated Learning in IoT and Edge Computing

  • Overview of Federated Learning and its applications in the Internet of Things (IoT)
  • Key challenges in integrating Federated Learning with edge computing for government operations
  • Benefits of decentralized artificial intelligence (AI) in IoT environments for government use

Federated Learning Techniques for IoT Devices

  • Deploying Federated Learning models on IoT devices for enhanced operational efficiency
  • Addressing non-independent and identically distributed (non-IID) data and limited computational resources
  • Optimizing communication protocols between IoT devices and central servers for government applications

Real-Time Decision-Making and Latency Reduction

  • Enhancing real-time processing capabilities in edge environments to support rapid decision-making for government operations
  • Techniques for reducing latency in Federated Learning systems to improve response times for government services
  • Implementing edge AI models for fast and reliable decision-making in critical government scenarios

Ensuring Data Privacy in Federated IoT Systems

  • Data privacy techniques in decentralized AI models to protect sensitive information for government use
  • Managing data sharing and collaboration across IoT devices while maintaining security for government operations
  • Compliance with data privacy regulations in IoT environments to ensure legal and ethical standards are met for government entities

Case Studies and Practical Applications

  • Successful implementations of Federated Learning in IoT systems for government projects
  • Practical exercises with real-world IoT datasets to train government personnel
  • Exploring future trends in Federated Learning for IoT and edge computing to inform strategic planning for government initiatives

Summary and Next Steps

Requirements

  • Experience in Internet of Things (IoT) or edge computing development for government projects
  • Basic understanding of artificial intelligence and machine learning principles
  • Familiarity with distributed systems and network protocols

Audience

  • IoT engineers working in the public sector
  • Edge computing specialists for government initiatives
  • AI developers supporting government programs
 14 Hours

Number of participants


Price per participant

Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.

Upcoming Courses

Federated Learning in IoT and Edge Computing

2026-02-24 09:30
14 hours
Tucson, AZ - Regus – One South Church Downtown
$ 2703 (Online)
$ 3903 (Classroom)

Federated Learning in IoT and Edge Computing

2026-03-10 09:30
14 hours
Tulsa, Ok
$ 2703 (Online)
$ 3903 (Classroom)

Federated Learning in IoT and Edge Computing

2026-03-24 09:30
14 hours
75 S West Temple, Salt Lake City, UT
$ 2703 (Online)
$ 3903 (Classroom)

Federated Learning in IoT and Edge Computing

2026-04-07 09:30
14 hours
Winooski, VT – MyCube at Champlain Mill
$ 2703 (Online)
$ 3903 (Classroom)

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