SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning. (arXiv:2311.13174v1 [cs.LG])

In response to legislation mandating companies to honor the \textit{right to
be forgotten} by erasing user data, it has become imperative to enable data
removal in Vertical Federated Learning (VFL) where multiple parties provide
private features for model training. In VFL, data removal, i.e.,
\textit{machine unlearning}, often requires removing specific features across
all samples under privacy guarentee in federated learning. To address this
challenge, we propose \methname, a novel Gradient Boosting Decision Tree (GBDT)
framework that effectively enables both \textit{instance …

companies data data removal decision enable features federated federated learning legislation machine model training private response right to be forgotten training trees user data