A Collaboration Strategy in the Mining Pool for Proof-of-Neural-Architecture Consensus
Boyang Li, Qing Lu, Weiwen Jiang, Taeho Jung, Yiyu Shi

TL;DR
This paper introduces a novel mining pool strategy for blockchain systems where miners collaboratively perform neural architecture search tasks, improving performance and reliability in proof-of-neural-architecture consensus mechanisms.
Contribution
It proposes the first mining pool solution tailored for blockchain consensuses based on deep learning, specifically Neural Architecture Search, enhancing competitiveness and task completion reliability.
Findings
Mining pool performance surpasses individual miners.
Partitioning search space improves efficiency.
Backup miners ensure task completion despite miner variability.
Abstract
In most popular public accessible cryptocurrency systems, the mining pool plays a key role because mining cryptocurrency with the mining pool turns the non-profitable situation into profitable for individual miners. In many recent novel blockchain consensuses, the deep learning training procedure becomes the task for miners to prove their workload, thus the computation power of miners will not purely be spent on the hash puzzle. In this way, the hardware and energy will support the blockchain service and deep learning training simultaneously. While the incentive of miners is to earn tokens, individual miners are motivated to join mining pools to become more competitive. In this paper, we are the first to demonstrate a mining pool solution for novel consensuses based on deep learning. The mining pool manager partitions the full searching space into subspaces and all miners are…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Brain Tumor Detection and Classification · Stochastic Gradient Optimization Techniques
Methodstravel james
