FedFlex: Protecting Shared Features in Vertical Federated Learning via Differential Privacy

Vertical federated learning (VFL, a method where multiple parties train an AI model together by sharing features derived from their local data without sharing the raw data itself) can leak sensitive information through the shared features, making them vulnerable to attacks like reconstruction and inference (where attackers try to figure out or recreate the original data). FedFlex is a new framework that protects these shared features by combining VFL with differential privacy (DP, a technique that adds noise to data to hide individual information), first adding a fixed amount of noise and then automatically adjusting how features are shared to improve accuracy while maintaining privacy protection.