  We present a privacy-preserving distributed learning framework for telecom
ecosystems in the 6G-era that enables the vision of shared ownership and
governance of ML models, while protecting the privacy of the data owners. We
demonstrate its benefits by applying it to the use-case of Quality of
Transmission (QoT) estimation in multi-domain multi-vendor optical networks,
where no data of individual domains is shared with the network management
system (NMS).
