A Collaboration Strategy in the Mining Pool for Proof-of-Neural-Architecture Consensus
This addresses the need for efficient mining pools in blockchain systems using deep learning consensuses, but it is incremental as it adapts existing mining pool concepts to a new context.
The paper tackles the problem of making deep learning-based blockchain mining pools competitive by partitioning the Neural Architecture Search (NAS) space and scheduling miners to collaborate, resulting in a mining pool that outperforms individual miners.
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 scheduled to collaborate on the Neural Architecture Search (NAS) tasks in the assigned subspace. Experiments demonstrate that the performance of this type of mining pool is more competitive than an individual miner. Due to the uncertainty of miners' behaviors, the mining pool manager checks the standard deviation of the performance of high reward miners and prepares backup miners to ensure the completion of the tasks of high reward miners.