论文标题
在5G移动网络中安全联合学习
Secure Federated Learning in 5G Mobile Networks
论文作者
论文摘要
机器学习（ML）是优化，保护和管理移动网络的重要推动力。这导致了从网络功能中收集和处理数据的增加，这反过来可能会增加对敏感最终用户信息的威胁。因此，需要减少对最终用户隐私威胁的机制充分利用ML。我们将联合学习（FL）无缝集成到3GPP 5G网络数据分析（NWDA）体系结构中，并添加多方计算（MPC）协议，以保护本地更新的机密性。我们评估该协议并发现它的开销比以前的工作要低得多，而不会影响ML性能。
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.
