标题： 数据证明：一种协作智能的共识协议 显示英文标题

标题： Proof-of-Data: A Consensus Protocol for Collaborative Intelligence

摘要： 现有的联邦学习研究主要集中在由中心实体协调的学习设置中。 然而，未来协作智能的最大潜力将在一个更加开放和民主化的环境中释放，其中没有中心实体占据主导地位，这种环境被称为“去中心化联邦学习”。 在这种情况下，实现所有参与节点的正确模型训练和公平奖励分配带来了新的挑战，尤其是拜占庭节点的威胁可能会危及这两项任务。 在本文中，我们提出了一种基于区块链的去中心化拜占庭容错联邦学习框架，该框架基于一种新颖的数据证明（PoD）共识协议，以解决“信任”和“激励”两个方面的问题。 通过将模型训练和贡献核算解耦，PoD不仅能够从异步社会规模的类似工作量证明（PoW）的学习中受益于学习效率和系统活力，还能从基于周期的拜占庭容错（BFT）式投票中获得共识和奖励分配的最终性。 为了缓解拜占星攻击带来的数据伪造导致的虚假奖励声明，设计了一个隐私感知的数据验证和基于贡献的奖励分配机制，以完善该框架。 我们的评估结果表明，PoD在模型训练方面的性能接近集中式 counterparts，同时在共识中实现了信任，并在奖励分配中实现了公平性，故障容忍比为1/3。 显示英文摘要

摘要： Existing research on federated learning has been focused on the setting where learning is coordinated by a centralized entity. Yet the greatest potential of future collaborative intelligence would be unleashed in a more open and democratized setting with no central entity in a dominant role, referred to as "decentralized federated learning". New challenges arise accordingly in achieving both correct model training and fair reward allocation with collective effort among all participating nodes, especially with the threat of the Byzantine node jeopardising both tasks. In this paper, we propose a blockchain-based decentralized Byzantine fault-tolerant federated learning framework based on a novel Proof-of-Data (PoD) consensus protocol to resolve both the "trust" and "incentive" components. By decoupling model training and contribution accounting, PoD is able to enjoy not only the benefit of learning efficiency and system liveliness from asynchronous societal-scale PoW-style learning but also the finality of consensus and reward allocation from epoch-based BFT-style voting. To mitigate false reward claims by data forgery from Byzantine attacks, a privacy-aware data verification and contribution-based reward allocation mechanism is designed to complete the framework. Our evaluation results show that PoD demonstrates performance in model training close to that of the centralized counterpart while achieving trust in consensus and fairness for reward allocation with a fault tolerance ratio of 1/3.