【Author】
Liu, Yuan; Lan, Yixiao; Li, Boyang; Miao, Chunyan; Tian, Zhihong
【Source】COMPUTER NETWORKS
【Abstract】The advent of neural network (NN) based deep learning, especially the recent development of the automatic design of networks, has brought unprecedented performance gains at heavy computational cost. On the other hand, in order to generate a new consensus block, Proof of Work (PoW) based blockchain systems routinely perform a huge amount of computation that does not achieve practical purposes but to solving a difficult cryptographic hash puzzle problem.In this study, we propose a new consensus mechanism, Proof of Learning (PoLe), which directs the computation spent for block consensus toward optimization of neural networks. In our design, the training and testing data are released to the entire blockchain network and the consensus nodes train NN models on the data, which serves as the proof of learning. As a core component of PoLe, we design a secure mapping layer (SML) to prevent consensus nodes from cheating, which can be straightforwardly implemented as a linear NN layer. When the consensus on the blockchain network is achieved, a new block is appended to the blockchain. We experimentally compare the PoLe protocol with Proof of Work (PoW) and show that PoLe can achieve a more stable block generation rate, which leads to more efficient transaction processing. Experimental evaluation also shows the PoLe can achieve a stable block generation rate without significantly sacrificing training performance.
【Keywords】Consensus mechanism; Proof of Learning; Secure mapping layer
【标题】学习证明 (PoLe)：通过在区块链上建立共识来增强神经网络训练
【摘要】基于神经网络 (NN) 的深度学习的出现，尤其是网络自动设计的最新发展，以巨大的计算成本带来了前所未有的性能提升。另一方面，为了生成新的共识块，基于工作量证明（PoW）的区块链系统通常会执行大量计算，这些计算并没有达到实际目的，而是解决了一个困难的密码哈希难题。在这项研究中，我们提出了一种新的共识机制，学习证明（PoLe），它将用于块共识的计算引导到神经网络的优化。在我们的设计中，训练和测试数据发布到整个区块链网络，共识节点在数据上训练NN模型，作为学习的证明。作为 PoLe 的核心组件，我们设计了一个安全映射层（SML）来防止共识节点作弊，它可以直接实现为线性 NN 层。当在区块链网络上达成共识时，一个新的区块被附加到区块链上。我们通过实验将 PoLe 协议与工作量证明 (PoW) 进行比较，结果表明 PoLe 可以实现更稳定的区块生成率，从而实现更高效的交易处理。实验评估还表明，Pole 可以在不显着牺牲训练性能的情况下实现稳定的块生成率。
评论