Secure Federated Learning in 5G networks

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 Computation (MPC) protocol for protecting the confidentiality of local updates. We evaluate the protocol and find that it has much lower overhead than previous work, without affecting ML performance.

Research paper

Full abstract available in IEEEXplore DOI: 10.1109/GLOBECOM42002.2020.9322479

Authors:

Martin Isaksson and Karl Norrman

Presented at the IEEE Global Communications Conference (GLOBECOMM 2020), December, 2020.

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Secure Federated Learning in 5G Mobile Networks

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