Federated unlearning has emerged as a promising paradigm to erase the
client-level data effect without affecting the performance of collaborative
learning models. However, the federated unlearning process often introduces
extensive storage overhead and consumes substantial computational resources,
thus hindering its implementation in practice. To address this issue, this
paper proposes a scalable federated unlearning framework based on isolated
sharding and coded computing. We first divide distributed clients into multiple
isolated shards across stages to reduce the number of clients being affected.
Then, to reduce the storage overhead of the central server, we develop a coded
computing mechanism by compressing the model parameters across different
shards. In addition, we provide the theoretical analysis of time efficiency and
storage effectiveness for the isolated and coded sharding. Finally, extensive
experiments on two typical learning tasks, i.e., classification and generation,
demonstrate that our proposed framework can achieve better performance than
three state-of-the-art frameworks in terms of accuracy, retraining time,
storage overhead, and F1 scores for resisting membership inference attacks.

提出了一种基于分散隔离和编码计算的可扩展联邦遗忘框架，通过将分布式客户端分为多个隔离片段以减少受影响的客户端数量，并通过压缩不同片段之间的模型参数来减少中央服务器的存储开销，最后，在两个典型的学习任务上进行了广泛实验，表明我们的框架在准确性、重训练时间、存储开销和抵抗成员攻击的 F1 分数方面都优于三种最先进的框架。