Book contents
- Frontmatter
- Contents
- List of Figures
- List of Tables
- 1 Introduction
- 2 Preliminary
- 3 Fundamental Theory and Algorithms of Edge Learning
- 4 Communication-Efficient Edge Learning
- 5 Computation Acceleration
- 6 Efficient Training with Heterogeneous Data Distribution
- 7 Security and Privacy Issues in Edge Learning Systems
- 8 Edge Learning Architecture Design for System Scalability
- 9 Incentive Mechanisms in Edge Learning Systems
- 10 Edge Learning Applications
- Bibliography
- Index
10 - Edge Learning Applications
Published online by Cambridge University Press: 14 January 2022
Book contents
- Frontmatter
- Contents
- List of Figures
- List of Tables
- 1 Introduction
- 2 Preliminary
- 3 Fundamental Theory and Algorithms of Edge Learning
- 4 Communication-Efficient Edge Learning
- 5 Computation Acceleration
- 6 Efficient Training with Heterogeneous Data Distribution
- 7 Security and Privacy Issues in Edge Learning Systems
- 8 Edge Learning Architecture Design for System Scalability
- 9 Incentive Mechanisms in Edge Learning Systems
- 10 Edge Learning Applications
- Bibliography
- Index
Summary
Big data and AI are enabling technologies for smart decision-making, automation, and resource optimization. These technologies collectively promote intelligent services from concepts to practical applications. It is widely recognized that Intelligent Services meet the strategic development of emerging industries, meanwhile enrich people’s lifestyle and make a convenient and efficient life. This chapter introduces the popular programming frameworks for Edge Learning. Then, we give some examples of emerging intelligent applications in the edge, e.g., smart health, self-driving, smart surveillance, and smart transportation.
- Type
- Chapter
- Information
- Edge Learning for Distributed Big Data AnalyticsTheory, Algorithms, and System Design, pp. 171 - 189Publisher: Cambridge University PressPrint publication year: 2022