Federated unlearning is a promising paradigm for protecting the data ownership of distributed clients. It allows central servers to remove historical data effects within the machine learning model as well as address the "right to be forgotten" issue in federated learning. However, existing works require central servers to retain the historical model parameters from distributed clients, such that allows the central server to utilize these parameters for further training even, after the clients exit the training process. To address this issue, this paper proposes a new blockchain-enabled trustworthy federated unlearning framework. We first design a proof of federated unlearning protocol, which utilizes the Chameleon hash function to verify data removal and eliminate the data contributions stored in other clients' models. Then, an adaptive contribution-based retraining mechanism is developed to reduce the computational overhead and significantly improve the training efficiency. Extensive experiments demonstrate that the proposed framework can achieve a better data removal effect than the state-of-the-art frameworks, marking a significant stride towards trustworthy federated unlearning.

翻译：联邦遗忘是一种保护分布式客户端数据所有权的有前景范式。它允许中央服务器移除机器学习模型中的历史数据影响，同时解决联邦学习中的“被遗忘权”问题。然而，现有工作需中央服务器保留来自分布式客户端的历史模型参数，这使得中央服务器在客户端退出训练后仍可利用这些参数进行进一步训练。针对此问题，本文提出了一种新型基于区块链的可信联邦遗忘框架。我们首先设计了联邦遗忘证明协议，该协议利用变色龙哈希函数验证数据删除并消除存储在其他客户端模型中的数据贡献。随后，我们开发了一种基于贡献的自适应重训练机制，以降低计算开销并显著提升训练效率。大量实验表明，所提框架在数据移除效果上优于现有最优框架，标志着在可信联邦遗忘领域迈出了重要一步。