Secure Federated Learning in 5G Mobile Networks

Secure Federated Learning in 5G Mobile Networks

 
 
 IEEE GLOBECOM 2020
 Secure Federated Learning in 5G Mobile Networks
 DOI: 10.1109/GLOBECOM42002.2020.9322479

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.

Secure Federated Learning in 5G Mobile Networks poster.
Secure Federated Learning in 5G Mobile Networks poster.

Background

This is a video for a technical report done for the 2020 WASP Project Course.

Suggested Citation

@inproceedings{isaksson2020secure,
  title         = {{Secure Federated Learning in 5G Mobile Networks}},
  author        = {Martin Isaksson and Karl Norrman},
  year          = {2020},
  booktitle     = {{IEEE} Global Communications Conference ({GLOBECOM})},
  publisher     = {{IEEE}},
  pages         = {1--6},
  doi           = {10.1109/GLOBECOM42002.2020.9322479},
}

Available

 Secure Federated Learning in 5G Mobile Networks
 DOI: 10.1109/GLOBECOM42002.2020.9322479
LinkedIn Reddit

Revisions