Forgetting Any Data at Any Time: A Theoretically Certified Unlearning Framework for Vertical Federated Learning

Understand

Privacy concerns in machine learning are heightened by regulations such as the GDPR, which enforces the "right to be forgotten" (RTBF), driving the emergence of machine unlearning as a critical research field.

• Vertical Federated Learning (VFL) enables collaborative model training by aggregating a sample's features across distributed parties while preserving data privacy at each source.
• This paradigm has seen widespread adoption in healthcare, finance, and other privacy-sensitive domains.
• However, existing VFL systems lack robust mechanisms to comply with RTBF requirements, as unlearning methodologies for VFL remain underexplored.