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#federatedlearning

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Aurélien Bellet<p>Time for a proper <a href="https://sigmoid.social/tags/introduction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>introduction</span></a>! 👋 I'm a <a href="https://sigmoid.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MachineLearning</span></a> researcher at Inria, France, focusing on <a href="https://sigmoid.social/tags/TrustworthyAI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>TrustworthyAI</span></a>—especially <a href="https://sigmoid.social/tags/decentralization" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>decentralization</span></a> &amp; <a href="https://sigmoid.social/tags/privacy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>privacy</span></a>. I design algorithms that learn from <a href="https://sigmoid.social/tags/decentralized" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>decentralized</span></a> data (<a href="https://sigmoid.social/tags/FederatedLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>FederatedLearning</span></a>) without memorizing personal data (<a href="https://sigmoid.social/tags/DifferentialPrivacy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DifferentialPrivacy</span></a>), working toward putting <a href="https://sigmoid.social/tags/data" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>data</span></a> &amp; <a href="https://sigmoid.social/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AI</span></a> back in people's hands. Maybe one day, these ideas could be used in the <a href="https://sigmoid.social/tags/Fediverse" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Fediverse</span></a>!</p>
Cory Doctorow<p>These standalone, "toy" models are derived from the big models, though. When the AI bubble bursts and the private sector no longer subsidizes mass-scale model creation, it will cease to spin out more sophisticated models that run on commodity hardware (it's possible that <a href="https://mamot.fr/tags/FederatedLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>FederatedLearning</span></a> and other techniques for spreading out the work of making large-scale models will fill the gap).</p><p>25/</p>
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