Blockchain-enabled Trustworthy Federated Unlearning
Yijing Lin Zhipeng Gao Hongyang Du Jinke Ren Zhiqiang Xie Dusit Niyato
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.