Verified Commit 9dc09f60 authored by Martin Isaksson's avatar Martin Isaksson
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Remove abstract from publication

parent 41bccc53
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......@@ -9,14 +9,6 @@ tags:
- privacy
date: 2020-01-14
online: https://arxiv.org/
bibtex: |
@misc{8369000,
author={Isaksson, Martin and Norrman, Karl},
title={Secure Federated Learning in 5G Mobile Networks},
year={2019},
volume={},
number={},
}
image:
path: /images/blog-ai.jpg
---
......@@ -25,16 +17,4 @@ image:
This is a video for a technical report done for the 2020 WASP Project Course.
## Abstract
Machine Learning 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
leak sensitive information about end-users. Consequently, mechanisms to
protect the privacy of end-users are needed. We seamlessly
integrate Federated Learning into the 3GPP 5G Network Data Analytics
framework, and add a multi-party computation protocol for protecting the
confidentiality of local updates. We also outline how our work can be fitted
into the WARA-PS research arena.
<p><iframe width="800" height="450" src="https://www.youtube.com/embed/JmuZcVAtLKY" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe></p>
{% include responsive-embed url="https://www.youtube.com/embed/JmuZcVAtLKY" ratio="16:9" %}
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