Federated unlearning (FU) enables the removal of specific data contributions from federated learning (FL) models to comply with regulations such as the General Data Protection Regulation (GDPR). However, most existing FU methods are designed for the FedAvg paradigm, where all clients share a single global model. In practice, personalized federated learning (pFL) methods such as FedPer, FedRep, Ditto, and FedBN have become widely adopted due to their superior handling of non-IID data. These methods decompose the model into shared global layers and client-specific personalized layers, fundamentally altering the semantics of unlearning, yet this setting has received little attention. We formalize FU under the pFL paradigm, identifying a tension between unlearning completeness on shared layers and personalization preservation for remaining clients. We then propose pFedUL, a layer-aware selective unlearning framework comprising three components: (1) gradient-based layer-wise contribution attribution that separately quantifies the target client's influence on shared and personalized parameters, (2) adaptive selective unlearning that applies differentiated forgetting strategies across layer types, and (3) a lightweight recalibration protocol enabling remaining clients to restore personalization with minimal overhead. We further introduce two new metrics, Personalization Preservation Score (PPS) and Cross-client Fairness Index (CFI), to evaluate pFL-specific unlearning quality. Experiments on CIFAR-10, CIFAR-100, and FEMNIST under varying non-IID settings indicate that pFedUL achieves unlearning effectiveness comparable to full retraining while maintaining an average of 97.3\% personalized accuracy for remaining clients. Compared with six state-of-the-art FU methods adapted to the pFL setting, pFedUL consistently achieves superior personalization preservation.

翻译：联邦遗忘（FU）能够从联邦学习（FL）模型中移除特定数据贡献，以符合《通用数据保护条例》（GDPR）等法规要求。然而，现有的大多数FU方法均针对FedAvg范式设计，该范式下所有客户端共享单一全局模型。实际中，FedPer、FedRep、Ditto和FedBN等个性化联邦学习（pFL）方法因在处理非独立同分布数据方面具有卓越表现而得到广泛应用。这些方法将模型分解为共享全局层和客户端专属个性化层，从根本上改变了遗忘机制，但这一设定尚未受到充分关注。本文对pFL范式下的FU进行了形式化定义，揭示了共享层上的遗忘完备性与剩余客户端的个性化保持之间存在矛盾。为此，我们提出pFedUL——一种层级感知的选择性遗忘框架，包含三个组件：（1）基于梯度的层级贡献归因，分别量化目标客户端对共享参数和个性化参数的影响；（2）自适应选择性遗忘，对不同层级类型施加差异化遗忘策略；（3）轻量级校准协议，使剩余客户端以最小开销恢复个性化性能。此外，我们引入两个新指标——个性化保持分数（PPS）和跨客户端公平性指数（CFI），以评估pFL特定的遗忘质量。在CIFAR-10、CIFAR-100和FEMNIST上的实验表明，在多种非独立同分布设定下，pFedUL在实现与完整重训练相当的遗忘效果的同时，能够为剩余客户端保持平均97.3%的个性化准确率。与六种适配pFL场景的最新FU方法相比，pFedUL在个性化保持方面始终表现更优。