A Collaboration Strategy in the Mining Pool for Proof-of-Neural-Architecture Consensus

• URL: http://arxiv.org/abs/2206.07089v1
• Date: Thu, 5 May 2022 17:08:02 GMT
• Title: A Collaboration Strategy in the Mining Pool for Proof-of-Neural-Architecture Consensus
• Authors: Boyang Li, Qing Lu, Weiwen Jiang, Taeho Jung, Yiyu Shi
• Abstract summary: 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. While the incentive of miners is to earn tokens, individual miners are motivated to join mining pools to become more competitive.
• Score: 16.372941299296652
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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 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.

Related papers

• Model-based Analysis of Mining Fairness in a Blockchain [2.9281463284266973]
  Mining fairness in blockchain refers to the equality between the computational resources invested in mining and the block rewards received. We propose a method to calculate mining fairness using a simple mathematical model. We validated that our method accurately computes mining fairness in networks with a small number of miners.
  arXiv  Detail & Related papers  (2024-06-02T02:27:28Z)
• Smoothening block rewards: How much should miners pay for mining pools? [11.245119287096419]
  We quantify the economic advantage for a given miner of having smooth rewards. We use this to define a maximum percentage of rewards that a miner should be willing to pay for the mining pool services.
  arXiv  Detail & Related papers  (2023-09-05T14:59:01Z)
• FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs [57.12856172329322]
  We envision a decentralized system unlocking the potential vast untapped consumer-level GPU. This system faces critical challenges, including limited CPU and GPU memory, low network bandwidth, the variability of peer and device heterogeneity.
  arXiv  Detail & Related papers  (2023-09-03T13:27:56Z)
• Cobalt: Optimizing Mining Rewards in Proof-of-Work Network Games [6.052883613180156]
  A key factor affecting mining rewards earned is the connectivity between miners in the peer-to-peer network. We formulate the problem of deciding whom to connect to for miners as a bandit problem. A key contribution of our work is the use of network coordinates based model for learning the network structure within the bandit algorithm.
  arXiv  Detail & Related papers  (2023-07-10T16:50:58Z)
• Partial Selfish Mining for More Profits [21.636578888742477]
  Mining attacks aim to gain an unfair share of extra rewards in the blockchain mining. In this paper, we propose a new and feasible Partial Selfish Mining (PSM) attack. We show that PSM attackers can be more profitable than selfish miners under a certain range of mining power and network conditions.
  arXiv  Detail & Related papers  (2022-07-27T11:58:38Z)
• AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling [82.08631594071656]
  Pooling layers are essential building blocks of Convolutional Neural Networks (CNNs) We propose an adaptive and exponentially weighted pooling method named adaPool. We demonstrate how adaPool improves the preservation of detail through a range of tasks including image and video classification and object detection.
  arXiv  Detail & Related papers  (2021-11-01T08:50:37Z)
• The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors [62.9301667732188]
  We propose a second iteration of the MineRL Competition. The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment. At the end of each round, competitors submit containerized versions of their learning algorithms to the AIcrowd platform.
  arXiv  Detail & Related papers  (2021-01-26T20:32:30Z)
• Quantum Multi-Solution Bernoulli Search with Applications to Bitcoin's Post-Quantum Security [67.06003361150228]
  A proof of work (PoW) is an important cryptographic construct enabling a party to convince others that they invested some effort in solving a computational task. In this work, we examine the hardness of finding such chain of PoWs against quantum strategies. We prove that the chain of PoWs problem reduces to a problem we call multi-solution Bernoulli search, for which we establish its quantum query complexity.
  arXiv  Detail & Related papers  (2020-12-30T18:03:56Z)
• Faster Secure Data Mining via Distributed Homomorphic Encryption [108.77460689459247]
  Homomorphic Encryption (HE) is receiving more and more attention recently for its capability to do computations over the encrypted field. We propose a novel general distributed HE-based data mining framework towards one step of solving the scaling problem. We verify the efficiency and effectiveness of our new framework by testing over various data mining algorithms and benchmark data-sets.
  arXiv  Detail & Related papers  (2020-06-17T18:14:30Z)
• Equilibrium of Blockchain Miners with Dynamic Asset Allocation [4.030037871304249]
  We model and analyze blockchain miners who seek to maximize the compound return of their mining businesses. The cost of mining determines the share of each miner or mining pool at equilibrium. We conclude that neither miners nor mining pools who seek to maximize their compound return will have a financial incentive to occupy more than 50% of the hash rate.
  arXiv  Detail & Related papers  (2020-06-14T20:52:31Z)
• Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach [54.29213445674221]
  Federated Learning (BFL) enables mobile devices to collaboratively train neural network models required by a Machine Learning Model Owner (MLMO) The issue of BFL is that the mobile devices have energy and CPU constraints that may reduce the system lifetime and training efficiency. We propose to use the Deep Reinforcement Learning (DRL) to derive the optimal decisions for theO.
  arXiv  Detail & Related papers  (2020-04-08T16:29:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.