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Secure Federated Learning in 5G Mobile Networks
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0001-9972-0179
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Theoretical Computer Science, TCS.ORCID iD: 0000-0003-0164-1478
2020 (English)Manuscript (preprint) (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
2020. Vol. abs/2004.06700
National Category
Computer Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-273183OAI: oai:DiVA.org:kth-273183DiVA, id: diva2:1429371
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Note

QC 20200511

Available from: 2020-05-11 Created: 2020-05-11 Last updated: 2020-05-12Bibliographically approved

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Secure Federated Learning in 5G Mobile Networks

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Isaksson, MartinNorrman, Karl

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