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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Federated Learning in Mobile Edge Networks: A Comprehensive Survey · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1909.11875/</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/1909.11875/feed.xml" rel="self" type="application/rss+xml" /><item><title>Federated Learning in Mobile Edge Networks: A Comprehensive Survey — mentioned by sdenton4</title><link>https://news.ycombinator.com/item?id=30917693</link><guid isPermaLink="false">urn:hackerpapers:mention:1909.11875:30917693</guid><pubDate>Tue, 05 Apr 2022 11:35:13 GMT</pubDate><description>&lt;p&gt;It&amp;#x27;s likely worth looking into federated learning. You could have a core model, which then trains locally on player inputs. The federated learning research space is focused on this sort of small model update on phones, so should have plenty of people who have thought hard about the problem of keeping binary sizes small and training compute manageable. https://arxiv.org/abs/1909.11875&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1909.11875/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.NI</category></item></channel></rss>
