论文标题
学习证明（POL）：通过在区块链上建立共识的机器学习能力
Proof of Learning (PoLe): Empowering Machine Learning with Consensus Building on Blockchains
论文作者
论文摘要
深度学习的进步（DL），尤其是网络自动设计的最新发展，以大量的计算成本带来了前所未有的绩效增长。另一方面，区块链系统通常执行大量计算，这些计算无法实现实际目的，以建立分散参与者的工作证明（POW）共识。在本文中，我们提出了一种新的共识机制，即学习证明（POL），该机制将用于共识花费的计算用于优化神经网络（NN）。在我们的机制中，培训/测试数据将释放到整个区块链网络（BCN），并在数据上培训了共识节点NN模型，这是学习证明。当BCN上的共识认为NN模型是有效的时，将附加一个新区块。我们通过实验将POL协议与工作证明（POW）进行比较，并表明POL可以达到更稳定的块生成率，从而导致更有效的交易处理。我们还引入了一种新颖的作弊预防机制，安全映射层（SML），可以直接实现为线性NN层。经验评估表明，SML可以以较小的成本检测作弊节点，以实现预测性能。
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.
