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Machine Learning (ML) is an important enabler for optimizing, securing andmanaging mobile networks. This leads to increased collection and processing ofdata from network functions, which in turn may increase threats to sensitiveend-user information. Consequently, mechanisms to reduce threats to end-userprivacy are needed to take full advantage of ML. We seamlessly integrateFederated Learning (FL) into the 3GPP 5G Network Data Analytics (NWDA)architecture, and add a Multi-Party Computation (MPC) protocol for protectingthe confidentiality of local updates. We evaluate the protocol and find that ithas much lower overhead than previous work, without affecting ML performance.

Secure Federated Learning in 5G Mobile Networks