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.

翻译：区块链联邦学习（FL）因能确保FL过程的完整性和可追溯性而受到广泛关注。区块链FL要求参与者利用本地数据训练模型，随后将模型发布到区块链上，形成代表模型关系的类有向无环图（DAG）继承结构。然而，这种基于DAG的特殊结构因复杂性和开销较高，给涉及敏感数据的模型更新带来了挑战。为解决此问题，我们提出区块链联邦遗忘学习（BlockFUL）——一种通用框架，该框架利用变色龙哈希（CH）技术重新设计区块链结构，以降低模型更新的复杂性，从而减少遗忘任务的计算和共识成本。此外，BlockFUL支持多种联邦遗忘方法，确保模型更新（无论是并行还是串行执行）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遗忘方法进行了全面研究，展示了这两类方法中通过最少CH和区块更新操作实现的高效遗忘工作流。同时，我们比较了这些方法的计算与通信成本。