Federated Learning (FL) enables collaborative, privacy-preserving model training, but supporting the "Right to be Forgotten" is especially challenging because data influences the model through distributed and interleaved client updates. Existing exact unlearning methods typically require frequent retraining from scratch, resulting in high communication cost and long service downtime. To address this, we propose Federated Sequential Group-based Training (FedSGT), an exact unlearning framework for FL. FedSGT partitions the data into uniform groups, and each client may participate in multiple groups. To control communication overhead, each client can limit the number of groups it contributes to. FedSGT then trains multiple sequences of Parameter-Efficient Fine-Tuning (PEFT) modules, each corresponding to a different group permutation. Since the PEFT modules are lightweight and maintained server-side, FedSGT isolates the influence of different data groups into independent modules without incurring significant storage overhead and communication cost. Exact unlearning is thus achieved instantly by deactivating the modules corresponding to the group containing the unlearned data. Furthermore, using multiple training sequences helps maintain high model utility as deletion requests accumulate. We provide a rigorous theoretical analysis of both the deletion rate -- expected number of deletions before retraining is needed -- and the expected model performance. Experiments on various tasks demonstrate that FedSGT achieves a significantly longer service maintenance under multiple unlearning requests while maintaining comparable learning performance and training efficiency to other exact unlearning baselines. Extensive ablation studies validate the robustness of our method across a wide range of parameter settings.

翻译：联邦学习（Federated Learning, FL）支持协作式、隐私保护的模型训练，但实现“被遗忘权”尤其具有挑战性，因为数据通过分布式且交错进行的客户端更新影响模型。现有的精确遗忘方法通常需要频繁从头开始重新训练，导致高昂的通信成本和较长的服务停机时间。为解决这一问题，我们提出了联邦顺序分组训练（Federated Sequential Group-based Training, FedSGT），一种面向联邦学习的精确遗忘框架。FedSGT将数据划分为均匀的组，每个客户端可参与多个组。为控制通信开销，每个客户端可限制其参与的组数。FedSGT随后训练多组参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）模块序列，每个序列对应不同的组排列顺序。由于PEFT模块轻量且由服务器端维护，FedSGT将不同数据组的影响隔离到独立的模块中，而不会产生显著的存储开销和通信成本。因此，通过停用包含待遗忘数据组所对应的模块，即可即时实现精确遗忘。此外，使用多个训练序列有助于在删除请求累积时保持较高的模型效用。我们对删除率（即需要重新训练前的预期删除次数）和预期模型性能均进行了严格的理论分析。在不同任务上的实验表明，FedSGT在应对多次遗忘请求时能实现显著更长的服务维持时间，同时保持与其他精确遗忘基线方法相当的学习性能和训练效率。广泛的消融研究验证了我们的方法在多种参数设置下的鲁棒性。