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cs.LG2026
Erased, but Not Gone: Output Forgetting Is Not True Forgetting
Teresa Pui Yee Yong, Win Kent Ong, Chee Seng Chan
Machine unlearning (MU) is commonly judged by output forgetting, such as low forget-set accuracy or reduced logit-level membership inference. But if output-level success can coexis…
cs.LG2025
Ten Challenging Problems in Federated Foundation Models
Tao Fan, Hanlin Gu, Xuemei Cao +30
Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of fed…
cs.LG2024
Towards Privacy-Guaranteed Label Unlearning in Vertical Federated Learning: Few-Shot Forgetting without Disclosure
Hanlin Gu, Hong Xi Tae, Lixin Fan +1
This paper addresses the critical challenge of unlearning in Vertical Federated Learning (VFL), a setting that has received far less attention than its horizontal counterpart. Spec…