Privacy-Preserving Distributed Learning Framework for 6G Telecom Ecosystems
This addresses privacy concerns for telecom operators and data owners in 6G networks, though it appears incremental as it builds on existing distributed learning concepts.
The paper tackles the problem of enabling shared ownership and governance of ML models in 6G telecom ecosystems while protecting data privacy, and demonstrates its benefits by applying it to Quality of Transmission estimation in multi-domain optical networks without sharing individual domain data.
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).