Poster: Task Difficulty Adjustment in the Energy-Recycling Consensus Mechanism

Hao Zeng, Man Li, Helei Cui, Yuefeng Du, Zhiwen Yu, Bin Guo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

An increasing number of energy-recycling consensus mechanisms are being employed to address the drawback of proof of work (PoW) wasting computation and energy. For instance, the computing power wasted in solving difficult but meaningless PoW puzzles is used to conduct practical federated learning tasks and train deep learning models. However, there remains a neglected issue of task difficulty adjustment. To address this problem, we propose a method for measuring task difficulty and an algorithm for adjustment to achieve controlled minting and stable transaction processing capacity for cryptocurrency based on energy-recycling consensus mechanisms. Our research evaluates the effectiveness of this algorithm and highlights the potential benefits of this approach.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE 43rd International Conference on Distributed Computing Systems, ICDCS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1069-1070
Number of pages2
ISBN (Electronic)9798350339864
DOIs
StatePublished - 2023
Event43rd IEEE International Conference on Distributed Computing Systems, ICDCS 2023 - Hong Kong, China
Duration: 18 Jul 202321 Jul 2023

Publication series

NameProceedings - International Conference on Distributed Computing Systems
Volume2023-July

Conference

Conference43rd IEEE International Conference on Distributed Computing Systems, ICDCS 2023
Country/TerritoryChina
CityHong Kong
Period18/07/2321/07/23

Keywords

  • blockchain
  • consensus mechanism
  • federated learning
  • proof of learning

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