Vertical Federated Learning for Privacy-Preserving ML Model Development in Partially Disaggregated Networks

We present a novel framework that enables vendors and operators, with partial access to operational and monitoring features of a service, to collaboratively develop a ML-assisted solution without revealing any business-critical raw data to each other. We validate our proposal for a QoT estimation use-case.