Proof of Learning (PoLe): Empowering Machine Learning with Consensus Building on Blockchains (Demo)
Yixiao Lan, Yuan Liu, Boyang Li, Chunyan Miao
Abstract
The consensus algorithm is the core component of a blockchain system, which determines the efficiency, security, and scalability of the blockchain network. The representative consensus algorithm is the proof of work (PoW) proposed in Bitcoin, where the consensus process consumes large amount of compute in solving meaningless Hash puzzel. Meanwhile, the deep learning (DL) has brought unprecedented performance gains at heavy computate cost. In this demo, we channels the otherwise wasted computational power to the practical purpose of training neural network models, through the proposed proof of learning (PoL) consensus algorithm. In PoLe, the training/testing data are released to the entire blockchain network (BCN) and the consensus nodes train NN models on the data, which serves as the proof of learning. When the consensus on the BCN considers a NN model to be valid, a new block is appended to the blockchain. Through our system, we investigate the potential of enpowering machine learning with consensus building on blockchains.