Federated Learning(FL) is predominantly deployed in enterprise environments, where limited transparency and restricted auditability hinder broader adoption. Existing FL systems often suffer from opaque aggregation processes, making it unclear which model updates are accepted or discarded. Current mitigation strategies typically rely on external validators introducing additional computational and communication overhead. In this paper, we propose a novel FL framework that leverages existing Web3 technologies to enhance transparency, trust and auditability throughout the training process. The framework adopts a hierarchical architecture in which delegated managers orchestrate the FL training process within their respective federations. To mitigate adversarial and poisoning attacks, a combination of novelty detection and consensus mechanisms were employed. Model updates are encoded and broad casted to all managers, who independently evaluate their validity and those model updates that are approved by the consensus are incorporated into the global model. Additionally, a reputation score based backup mechanism is employed to ensure model generation. Extensive experiments conducted under real world scenarios demonstrate the effectiveness, resilience of the proposed framework, highlighting its potential to enable transparent FL beyond traditional enterprise setting.

翻译：[translated abstract in Chinese] 联邦学习（FL）主要部署在企业环境中，其有限的透明度和受限的可审计性阻碍了更广泛的采用。现有的FL系统通常存在聚合过程不透明的问题，使得哪些模型更新被接受或丢弃并不清晰。当前的缓解策略通常依赖外部验证器，这引入了额外的计算和通信开销。本文提出了一种新颖的FL框架，该框架利用现有的Web3技术来增强训练过程中的透明度、信任和可审计性。该框架采用分层架构，其中委派的管理者在其各自的联邦内协调FL训练过程。为减轻对抗和投毒攻击，联合采用了异常检测和共识机制。模型更新被编码并广播给所有管理者，由他们独立评估其有效性，通过共识批准的模型更新将被整合到全局模型中。此外，还采用了一种基于声誉得分的备份机制来确保模型生成。在真实场景下进行的广泛实验证明了所提框架的有效性和鲁棒性，突显了其在传统企业环境之外实现透明FL的潜力。