Blockchained Federated Learning (FL) has been gaining traction for ensuring
the integrity and traceability of FL processes. Blockchained FL involves
participants training models locally with their data and subsequently
publishing the models on the blockchain, forming a Directed Acyclic Graph
(DAG)-like inheritance structure that represents the model relationship.
However, this particular DAG-based structure presents challenges in updating
models with sensitive data, due to the complexity and overhead involved. To
address this, we propose Blockchained Federated Unlearning (BlockFUL), a
generic framework that redesigns the blockchain structure using Chameleon Hash
(CH) technology to mitigate the complexity of model updating, thereby reducing
the computational and consensus costs of unlearning tasks.Furthermore, BlockFUL
supports various federated unlearning methods, ensuring the integrity and
traceability of model updates, whether conducted in parallel or serial. We
conduct a comprehensive study of two typical unlearning methods, gradient
ascent and re-training, demonstrating the efficient unlearning workflow in
these two categories with minimal CH and block update operations. Additionally,
we compare the computation and communication costs of these methods.

使用变色龙哈希技术，通过重新设计区块链结构，我们提出了具有通用性的区块链联合取消学习框架 Blockchained Federated Unlearning (BlockFUL)，以减少模型更新的复杂性、计算和共识成本，并支持多种取消学习方法，确保模型更新的完整性和可追溯性。我们对梯度上升和重新训练这两种典型的取消学习方法进行了全面研究，证明了在这两个类别中以最少的变色龙哈希和区块更新操作实现高效的取消学习流程，并进一步比较了这些方法的计算和通信成本。