Federated learning (FL) enables collaborative model training over distributed private data. However, sustaining open participation requires incentive mechanisms that compensate contributors for their resources and risks. Enabled by Web3 primitives, especially blockchains, recent FL proposals incorporate incentive mechanisms for open participation, yet most focus primarily on algorithmic design and overlook system-level challenges, including coordination efficiency, secure handling of model updates, and practical usability. We present FWeb3, a practical Web3-enabled FL framework for incentive-aware training in open environments. FWeb3 adopts a modular architecture that separates FL functions from Web3 support services, decoupling the off-chain training and data plane from on-chain settlement while preserving verifiable incentive execution. The framework supports pluggable aggregation and contribution evaluation methods and provides a browser-native DApp interface to lower the participation barrier. We evaluate FWeb3 in real-world settings and show that it supports end-to-end incentive-aware FL with transaction and data-transfer overheads of only 21.3% and 3.4% in WAN; FWeb3 also deploys from zero configuration in under 3 minutes and enables user onboarding in under 1 minute.

翻译：联邦学习（FL）使得在分布式私有数据上进行协作模型训练成为可能。然而，维持开放参与需要激励机制，以补偿贡献者所付出的资源与承担的风险。借助Web3原语（尤其是区块链）的支持，近期的联邦学习方案已开始整合面向开放参与的激励机制，但多数工作主要聚焦于算法设计，而忽视了系统层面的挑战，包括协调效率、模型更新的安全处理以及实际可用性。本文提出FWeb3，一个实用的、基于Web3的联邦学习框架，用于在开放环境中进行激励感知的训练。FWeb3采用模块化架构，将联邦学习功能与Web3支持服务分离，使链下训练与数据平面同链上结算解耦，同时保持激励执行的可验证性。该框架支持可插拔的聚合与贡献评估方法，并提供浏览器原生的DApp界面以降低参与门槛。我们在真实场景中对FWeb3进行评估，结果表明它能够支持端到端的激励感知联邦学习，在广域网中其交易与数据传输开销分别仅为21.3%和3.4%；FWeb3还能在3分钟内从零配置完成部署，并在1分钟内实现用户接入。