Exploiting Internal Randomness for Privacy in Vertical Federated Learning

ePrint Report: Exploiting Internal Randomness for Privacy in Vertical Federated Learning

Yulian Sun, Li Duan, Ricardo Mendes, Derui Zhu, Yue Xia, Yong Li, Asja Fischer

Vertical Federated Learning (VFL) is becoming a standard collaborative learning paradigm with various practical applications. Randomness is essential to enhancing privacy in VFL, but introducing too much external randomness often leads to an intolerable performance loss. Instead, as it was demonstrated for other federated learning settings, leveraging internal randomness —as provided by variational autoencoders (VAEs) …

applications eprint report exploiting federated federated learning internal paradigm privacy randomness report standard