Federated learning (FL) enables learning a model from data distributed across numerous workers while preserving data privacy. However, the classical FL technique is designed for Web2 applications where participants are trusted to produce correct computation results. Moreover, classical FL workers are assumed to voluntarily contribute their computational resources and have the same learning speed. Therefore, the classical FL technique is not applicable to Web3 applications, where participants are untrusted and self-interested players with potentially malicious behaviors and heterogeneous learning speeds. This paper proposes Refiner, a novel blockchain-powered decentralized FL system for Web3 applications. Refiner addresses the challenges introduced by Web3 participants by extending the classical FL technique with three interoperative extensions: (1) an incentive scheme for attracting self-interested participants, (2) a two-stage audit scheme for preventing malicious behavior, and (3) an incentive-aware semi-synchronous learning scheme for handling heterogeneous workers. We provide theoretical analyses of the security and efficiency of Refiner. Extensive experimental results on the CIFAR-10 and Shakespeare datasets confirm the effectiveness, security, and efficiency of Refiner.

联邦学习 (FL) 可以从分布在众多工作人员中的数据中学习模型，同时保护数据隐私。然而，经典的 FL 技术是为 Web2 应用程序设计的，在这些应用程序中，参与者可以产生正确的计算结果。此外，经典的 FL 工作者被假设自愿贡献他们的计算资源并具有相同的学习速度。因此，经典的 FL 技术不适用于 Web3 应用程序，因为 Web3 应用程序的参与者是不受信任且自私的参与者，具有潜在的恶意行为和异构的学习速度。本文提出了Refiner，这是一种用于 Web3 应用程序的新型区块链驱动的去中心化 FL 系统。 Refiner 通过三个互操作扩展来扩展经典 FL 技术，解决了 Web3 参与者带来的挑战：(1) 用于吸引自利参与者的激励方案，(2) 用于防止恶意行为的两阶段审核方案，以及 (3)用于处理异构工作者的激励感知半同步学习方案。我们提供Refiner的安全性和效率的理论分析。 CIFAR-10 和 Shakespeare 数据集上的大量实验结果证实了 Refiner 的有效性、安全性和效率。