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.

中文翻译：

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联合学习的证明：一种新的能量回收共识算法

工作量证明（PoW）是区块链上最流行的共识机制，它需要大量的能量，但是除了确定矿工之间的会计权利外，没有任何有用的结果。为了解决PoW的缺点，我们提出了一种新颖的能量回收共识算法，即联邦学习证明（PoFL），该方法最初将浪费掉的能量用于解决PoW中困难但毫无意义的难题，然后将其重新投资到联邦学习中。联合学习和集中挖掘是PoW的一种趋势，在组织结构方面很自然。但是，区块链中数据使用权和所有权之间的分离导致模型训练和验证中的数据隐私泄漏，这与联邦学习的初衷背道而驰。为了应对挑战，提出了一种基于反向博弈的数据交易机制和一种隐私保护模型验证机制。前者可以防止训练数据泄漏，而后者可以通过隐私保护任务请求者的测试数据以及池提交的模型来验证训练模型的准确性。据我们所知，我们的文章是第一篇将联邦学习作为区块链工作证明的工作。基于合成数据和真实数据的大量仿真证明了我们提出的机制的有效性和效率。据我们所知，我们的文章是第一篇将联邦学习作为区块链工作证明的工作。基于合成数据和真实数据的大量仿真证明了我们提出的机制的有效性和效率。据我们所知，我们的文章是第一篇将联邦学习作为区块链工作证明的工作。基于合成数据和真实数据的大量仿真证明了我们提出的机制的有效性和效率。