Incentive-driven long-term optimization for edge learning by hierarchical reinforcement mechanism

Yi Liu, Leijie Wu, Yufeng Zhan, Song Guo, Zicong Hong

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Edge Learning is an emerging distributed machine learning in mobile edge network. Limited works have designed mechanisms to incentivize edge nodes to participate in edge learning. However, their mechanisms only consider myopia optimization on resource consumption, which results in the lack of learning algorithm performance guarantee and longterm sustainability. In this paper, we propose Chiron, an incentive-driven long-term mechanism for edge learning based on hierarchical deep reinforcement learning. First, our optimization goal combines learning-algorithms metric (i.e., model accuracy) with system metric (i.e., learning time, and resource consumption), which can improve edge learning quality under a fixed training budget. Second, we present a two-layer H-DRL design with exterior and inner agents to achieve both long-term and short-term optimization for edge learning, respectively. Finally, experiments on three different real-world datasets are conducted to demonstrate the superiority of our proposed approach. In particular, compared with the state-of-the-art methods under the same budget constraint, the final global model accuracy and time efficiency can be increased by 6.5 % and 39 %, respectively. Our implementation is available at https://github.com/Joey61Liuyi/Chiron.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 41st International Conference on Distributed Computing Systems, ICDCS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages35-45
Number of pages11
ISBN (Electronic)9781665445139
DOIs
Publication statusPublished - Jul 2021
Event41st IEEE International Conference on Distributed Computing Systems, ICDCS 2021 - Virtual, Washington, United States
Duration: 7 Jul 202110 Jul 2021

Publication series

NameProceedings - International Conference on Distributed Computing Systems
Volume2021-July

Conference

Conference41st IEEE International Conference on Distributed Computing Systems, ICDCS 2021
Country/TerritoryUnited States
CityVirtual, Washington
Period7/07/2110/07/21

Keywords

  • Deep Reinforcement Learning
  • Federated Learning
  • Incentive Mechanism
  • Mobile Edge Computing

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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