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An incentive mechanism for collaborative intelligence

Published: 20 October 2022 Publication History

Abstract

Collaborative intelligence is a new paradigm of distributed machine learning, in which a centralized server publishes an AI task and smart edge devices join together to complete the task. Applying collaborative intelligence in a wide range of applications has become a hot trend. However, it faces two intractable challenges: participant recruitment and service guarantee. In this paper, an incentive mechanism is proposed for collaborative intelligence. First, we employ a Stackelberg game model to analyze the participants' utility and design a winner-determination algorithm to select the optimal edge devices. Then, we study the long-term interactions of participants with a repeated game model and put forward a service-guarantee mechanism by setting up a provable trigger strategy. Finally, the simulation results validate our proposed strategies.

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cover image ACM Conferences
RACS '22: Proceedings of the Conference on Research in Adaptive and Convergent Systems
October 2022
208 pages
ISBN:9781450393980
DOI:10.1145/3538641
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 20 October 2022

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Author Tags

  1. collaborative intelligence
  2. incentive mechanism
  3. repeated game
  4. stackelberg game

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