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Machine Learning (ML) is an important enabler for optimizing, securing and managing mobile networks. This leads to increased collection and processing of data from network functions, which in turn may increase threats to sensitive end-user information. Consequently, mechanisms to reduce threats to end-user privacy are needed to take full advantage of ML. We seamlessly integrate Federated Learning (FL) into the 3GPP 5G Network Data Analytics (NWDA) architecture, and add a Multi-Party ComputationarXiv:2004.06700v2 fatcat:fx6za6tzbzeqdlayaiehiaypaa