Efficient Vertical Federated Learning with Secure Aggregation
Xinchi Qiu Heng Pan Wanru Zhao Chenyang Ma Pedro Porto Buarque de Gusmão Nicholas D. Lane
The majority of work in privacy-preserving federated learning (FL) has been
focusing on horizontally partitioned datasets where clients share the same sets
of features and can train complete models independently. However, in many
interesting problems, such as financial fraud detection and disease detection,
individual data points are scattered across different clients/organizations in
vertical federated learning. Solutions for this type of FL require the exchange
of gradients between participants and rarely consider privacy and security
concerns, posing a potential risk of privacy leakage. In this work, we present
a novel design for training vertical FL securely and efficiently using
state-of-the-art security modules for secure aggregation. We demonstrate
empirically that our method does not impact training performance whilst
obtaining 9.1e2 ~3.8e4 speedup compared to homomorphic encryption (HE).