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

arXiv: https://arxiv.org/abs/2206.07089 · HTML: https://mlsanity.com/paper/2206.07089/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 30 references that are in the map.
- First posted on arXiv 5 May 2022.

## Builds on (most cited first, 19 of 19)

- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search](https://mlsanity.com/paper/1812.03443/) (2018)
- [HAQ: Hardware-Aware Automated Quantization with Mixed Precision](https://mlsanity.com/paper/1811.08886/) (2018)
- [Single Path One-Shot Neural Architecture Search with Uniform Sampling](https://mlsanity.com/paper/1904.00420/) (2019)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [Neural Architecture Search with Bayesian Optimisation and Optimal Transport](https://mlsanity.com/paper/1802.07191/) (2018)
- [Hierarchical Representations for Efficient Architecture Search](https://mlsanity.com/paper/1711.00436/) (2017)
- [Simple And Efficient Architecture Search for Convolutional Neural Networks](https://mlsanity.com/paper/1711.04528/) (2017)
- [Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search](https://mlsanity.com/paper/1901.11211/) (2019)
- [Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithm](https://mlsanity.com/paper/1912.11745/) (2019)
- [A Study of the Human Perception of Synthetic Faces](https://mlsanity.com/paper/2111.04230/) (2021)
- [Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-based Distributed Deep Learning](https://mlsanity.com/paper/1903.09800/) (2019)
- [Energy-recycling Blockchain with Proof-of-Deep-Learning](https://mlsanity.com/paper/1902.03912/) (2019)
- [HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking](https://mlsanity.com/paper/1909.00122/) (2019)
- [DLBC: A Deep Learning-Based Consensus in Blockchains for Deep Learning Services](https://mlsanity.com/paper/1904.07349/) (2019)
- [Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation](https://mlsanity.com/paper/2008.07071/) (2020)

## Abstract (arXiv)

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.