PULSAR: Who-Forgets-Whom Personalized Unlearning in Federated Large Language Models

Federated unlearning (FU) has largely focused on producing a single unlearned global model, implicitly enforcing “forgetting for all” whenever any client requests deletion. In realistic federated deployments, however, data and model-sharing permissions are often client-specific and may change over time, requiring selective revocation of one client’s contribution from a subset of other clients. We propose PULSAR, the first who-forgets-whom personalized FU framework, which enables a source client to require a designated group of target clients to erase its contributions, while preserving the model utility of non-target clients that remain authorized or cooperative. Building on parameter-efficient federated fine-tuning, PULSAR constructs a portable, plug-and-play adapter-style knowledge filter from an unlearning operation and applies it at model dispatch to enforce client-specific forgetting. To mitigate utility degradation on target clients, we further introduce a joint optimization procedure to balance effective unlearning and utility retention. Single-round experimental results demonstrate that PULSAR achieves similar forgetting effect on targeted clients to Retrain baseline across multiple LLM backbones and FU settings, while maintaining similar model utility on non-target clients to the Finetune baseline.