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
9 - Incentive Mechanisms in Edge Learning Systems
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
How to build a benign ecosystem for sustainable development of edge learning is a crucial issue. This chapter first introduces incentive mechanisms for edge learning to motivate edge nodes to contribute model training. Specifically, in parameter server architecture, we introduce a deep reinforcement learning-based (DRL) incentive mechanism to determine the optimal pricing strategy for the parameter server and the optimal training strategies for edge nodes. Finally, we discuss future directions.
- Type
- Chapter
- Information
- Edge Learning for Distributed Big Data AnalyticsTheory, Algorithms, and System Design, pp. 159 - 170Publisher: Cambridge University PressPrint publication year: 2022