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.

翻译：联邦遗忘作为一种新兴范式，能够在消除客户端数据影响的同时不影响协作学习模型性能。然而，联邦遗忘过程常引入大量存储开销并消耗大量计算资源，从而阻碍其实际部署。针对该问题，本文提出一种基于隔离分片与编码计算的可扩展联邦遗忘框架。首先，通过将分布式客户端跨阶段划分为多个隔离分片，以减少受影响客户端数量。其次，为降低中央服务器存储开销，通过压缩不同分片间的模型参数，设计了一种编码计算机制。此外，本文对隔离分片与编码分片的时间效率与存储有效性进行了理论分析。最后，在分类与生成两类典型学习任务上的大量实验表明，与三个现有最优框架相比，本框架在准确性、重训练时间、存储开销及抵御成员推理攻击的F1分数方面均能实现更优性能。