4 papers
Client-Side Probing of Deleted Ridge Statistics in Federated Unlearning
Yijun Quan, Giovanni Montana
Federated unlearning aims to remove a client's data from a shared model without retraining from scratch. Some efficient systems make deletion exact by storing compact, additive sum…
Exact Federated Continual Unlearning for Ridge Heads on Frozen Foundation Models
Yijun Quan, Wentai Wu, Giovanni Montana
Foundation models are commonly deployed as frozen feature extractors with a small trainable head to adapt to private, user-generated data in federated settings. The ``right to be f…
\textsc{Lethe}: Principled Dual-Stream Update for Persistent Knowledge Erasure in Federated Unlearning
Wentai Wu, Hanwei Tan, Yijun Quan +4
Federated unlearning (FU) aims to erase knowledge from a global model. Existing studies commonly assume that federated collaboration terminates after unlearning, overlooking a depl…
Efficient Verified Machine Unlearning For Distillation
Yijun Quan, Zushu Li, Giovanni Montana
Growing data privacy demands, driven by regulations like GDPR and CCPA, require machine unlearning methods capable of swiftly removing the influence of specific training points. Al…