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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Federated Self-supervised Learning for Video Understanding · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2207.01975/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2207.01975/feed.xml" rel="self" type="application/rss+xml" /><item><title>Federated Self-supervised Learning for Video Understanding — mentioned by niclane7</title><link>https://news.ycombinator.com/item?id=35261225</link><guid isPermaLink="false">urn:hackerpapers:mention:2207.01975:35261225</guid><pubDate>Wed, 22 Mar 2023 14:09:21 GMT</pubDate><description>&lt;p&gt;Regarding federated labeling, you might be interested in some recent prototypes built on Flower that use forms of self supervised learning. By combining SSL with federated learning we can start to leverage unlabeled data and this will be a big deal once it becomes common place. I&amp;#x27;d suggest looking at these two research papers that build on Flower and include members of the Flower team as authors: https://arxiv.org/abs/2207.01975 https://arxiv.org/abs/2204.02804&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2207.01975/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
