Secure and Privacy-Preserving Vertical Federated Learning

ArXi:2604.13474v1 Announce Type: cross We propose a novel end-to-end privacy-preserving framework, instantiated by three efficient protocols for different deployment scenarios, covering both input and output privacy, for the vertically split scenario in federated learning (FL), where features are split across clients and labels are not shared by all parties.