vFedSec: Efficient Secure Aggregation for Vertical Federated Learning via Secure Layer. (arXiv:2305.16794v1 [cs.CR])

Most 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, individual data points are scattered across different
clients/organizations in a vertical setting. Solutions for this type of FL
require the exchange of intermediate outputs and gradients between
participants, posing a potential risk of privacy leakage when privacy and
security concerns are not considered. In this work, we …

aggregation clients data data points datasets features federated learning organizations privacy problems share solutions train work