Verified Commit 7102b738 authored by Martin Isaksson's avatar Martin Isaksson
Browse files

Update to publication template

parent b8e78264
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in 4 minutes and 6 seconds
......@@ -31,7 +31,6 @@ layout: default
<div class="page-content">
<div class="e-content">
<div class="publication">
<h2>Abstract</h2>
<div class="blog-post spacing">
{{ content }}
</div>
......@@ -39,8 +38,10 @@ layout: default
<h2>Suggested Citation</h2>
<!--<pre class="bibtex">{{- page.bibtex -}}</pre>-->
{% highlight bibtex %}{{page.bibtex}}{% endhighlight %}
<h2>Available</h2>
<a href="{{ page.online }}">{{page.title}}</a>
</div>
</div>
......
---
title: Secure Federated Learning in 5G Mobile Networks
author: M. Isaksson, K. Norrman
in: To appear in Arxiv
in: WASP Winter Conference 2020
tags:
- federated learning-
- machine learning-
- security protocol
- privacy
date: 2019-09-28
date: 2020-01-14
online: https://arxiv.org/
bibtex: |
@misc{8369000,
......@@ -18,9 +18,15 @@ bibtex: |
number={},
}
image:
path: /images/network.jpg
path: /images/blog-ai.jpg
---
## Background
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
......@@ -31,7 +37,4 @@ 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.
<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><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>
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