【Author】
Qu, Xidi; Wang, Shengling; Hu, Qin; Cheng, Xiuzhen
【Source】IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
【Abstract】Proof of work (PoW), the most popular consensus mechanism for blockchain, requires ridiculously large amounts of energy but without any useful outcome beyond determining accounting rights among miners. To tackle the drawback of PoW, we propose a novel energy-recycling consensus algorithm, namely proof of federated learning (PoFL), where the energy originally wasted to solve difficult but meaningless puzzles in PoW is reinvested to federated learning. Federated learning and pooled-mining, a trend of PoW, have a natural fit in terms of organization structure. However, the separation between the data usufruct and ownership in blockchain lead to data privacy leakage in model training and verification, deviating from the original intention of federal learning. To address the challenge, a reverse game-based data trading mechanism and a privacy-preserving model verification mechanism are proposed. The former can guard against training data leakage while the latter verifies the accuracy of a trained model with privacy preservation of the task requester's test data as well as the pool's submitted model. To the best of our knowledge, our article is the first work to employ federal learning as the proof of work for blockchain. Extensive simulations based on synthetic and real-world data demonstrate the effectiveness and efficiency of our proposed mechanisms.
【Keywords】Data models; Blockchain; Collaborative work; Training; Computational modeling; Training data; Task analysis; Blockchain; federated learning; consensus algorithm; incentive mechanism
【摘要】工作量证明（PoW）是区块链最流行的共识机制，它需要大量的能源，但除了确定矿工之间的会计权之外，没有任何有用的结果。为了解决 PoW 的缺点，我们提出了一种新的能量回收共识算法，即联邦学习证明 (PoFL)，将原本浪费在解决 PoW 中困难但无意义的难题的能量重新投入到联邦学习中。 PoW 的一种趋势是联邦学习和联合挖矿，在组织结构上有着天然的契合度。然而，区块链中数据使用权与所有权的分离导致模型训练和验证中的数据隐私泄露，背离了联邦学习的初衷。针对这一挑战，提出了一种基于反向博弈的数据交易机制和隐私保护模型验证机制。前者可以防止训练数据泄漏，而后者通过任务请求者的测试数据以及池提交的模型的隐私保护来验证训练模型的准确性。据我们所知，我们的文章是第一个使用联邦学习作为区块链工作证明的工作。基于合成和真实世界数据的广泛模拟证明了我们提出的机制的有效性和效率。
【关键词】数据模型；区块链；协作工作；训练;计算建模；训练数据；任务分析；区块链；联邦学习；共识算法；激励机制
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