Secure Federated Learning in 5G Mobile Networks
This addresses privacy threats for end-users in 5G networks, but it is incremental as it builds on existing FL and MPC methods.
The paper tackles the problem of protecting end-user privacy in machine learning for 5G mobile networks by integrating Federated Learning with a Multi-Party Computation protocol, resulting in much lower overhead without affecting ML performance.
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 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.