Secure Multi-Party Computation and Statistics Sharing for ML Model Training in Multi-domain Multi-vendor Networks
Pooyan Safari, Behnam Shariati, Geronimo Bergk, Johannes Fischer · 2021
We propose a secure aggregation algorithm that allows proprietary-owned domains, hosting statistically different datasets, train and operate ML models in a Horizontally Federated Learning fashion. The obtained results show a compelling test accuracy of 98.60% for a QoT estimation use-case in multi-domain multi-vendor networks