arXiv 2007.151452020-07-29cs.CRcs.LG

学习证明（PoLe）：通过在区块链上建立共识来增强机器学习

Proof of Learning (PoLe): Empowering Machine Learning with Consensus Building on Blockchains

Northeastern University, China（中国东北大学）；Alibaba-NTU Singapore Joint Research Institute（阿里巴巴-南洋理工大学新加坡联合研究院）

Yixiao Lan, Yuan Liu, Boyang Li

备注：: 11 pages,8 figures

摘要：

深度学习（DL）的进步，特别是最近网络自动设计的发展，以沉重的计算成本带来了前所未有的性能提升。另一方面，区块链系统通常会执行大量的计算，而这些计算并不能实现实际目的，以便从分散的参与者中建立工作量证明（PoW）共识。在本文中，我们提出了一个新的共识机制，学习证明（PoLe），它指导的计算花费的共识对神经网络（NN）的优化。在我们的机制中，训练/测试数据被发布到整个区块链网络（BCN），共识节点在数据上训练NN模型，作为学习的证据。当BCN上的共识认为NN模型有效时，一个新的区块被附加到区块链上。我们实验比较PoLe协议与工作量证明（PoW），并表明PoLe可以实现更稳定的块生成率，从而导致更有效的事务处理。我们还介绍了一种新的防作弊机制，安全映射层（SML），它可以直接实现为线性NN层。实验结果表明，SML算法能够以较小的代价检测出欺骗节点。

英文摘要：

The progress of deep learning (DL), especially the recent development of automatic design of networks, has brought unprecedented performance gains at heavy computational cost. On the other hand, blockchain systems routinely perform a huge amount of computation that does not achieve practical purposes in order to build Proof-of-Work (PoW) consensus from decentralized participants. In this paper, we propose a new consensus mechanism, Proof of Learning (PoLe), which directs the computation spent for consensus toward optimization of neural networks (NN). In our mechanism, 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. 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. We also introduce a novel cheating prevention mechanism, Secure Mapping Layer (SML), which can be straightforwardly implemented as a linear NN layer. Empirical evaluation shows that SML can detect cheating nodes at small cost to the predictive performance.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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英文摘要：

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