Federated Learning (FL) addresses the challenges posed by data silos, which arise from privacy, security regulations, and ownership concerns. Despite these barriers, FL enables these isolated data repositories to participate in collaborative learning without compromising privacy or security. Concurrently, the advancement of blockchain technology and decentralized applications (DApps) within Web 3.0 heralds a new era of transformative possibilities in web development. As such, incorporating FL into Web 3.0 paves the path for overcoming the limitations of data silos through collaborative learning. However, given the transaction speed constraints of core blockchains such as Ethereum (ETH) and the latency in smart contracts, employing one-shot FL, which minimizes client-server interactions in traditional FL to a single exchange, is considered more apt for Web 3.0 environments. This paper presents a practical one-shot FL system for Web 3.0, termed OFL-W3. OFL-W3 capitalizes on blockchain technology by utilizing smart contracts for managing transactions. Meanwhile, OFL-W3 utilizes the Inter-Planetary File System (IPFS) coupled with Flask communication, to facilitate backend server operations to use existing one-shot FL algorithms. With the integration of the incentive mechanism, OFL-W3 showcases an effective implementation of one-shot FL on Web 3.0, offering valuable insights and future directions for AI combined with Web 3.0 studies.

翻译：联邦学习（FL）旨在应对由隐私、安全法规和所有权问题所导致的数据孤岛挑战。尽管存在这些障碍，FL仍能使这些孤立的数据存储库在不损害隐私或安全的前提下参与协同学习。与此同时，区块链技术和去中心化应用（DApps）在Web 3.0中的发展预示着网络开发领域变革性可能的新时代。因此，将FL融入Web 3.0为通过协同学习克服数据孤岛的局限性开辟了道路。然而，考虑到以太坊（ETH）等核心区块链的交易速度限制以及智能合约的延迟，采用单轮FL——即将传统FL中的客户端-服务器交互简化为单次交换——被认为更适合Web 3.0环境。本文提出了一种面向Web 3.0的实用单轮FL系统，命名为OFL-W3。OFL-W3利用区块链技术，通过智能合约管理交易。同时，OFL-W3采用星际文件系统（IPFS）结合Flask通信，以促进后端服务器运行现有的单轮FL算法。通过集成激励机制，OFL-W3展示了在Web 3.0上有效实现单轮FL的实践，为人工智能与Web 3.0相结合的研究提供了有价值的见解和未来方向。