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		<title>IEEE TNNLS on Minghui Chen</title>
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				<title>Forgettable Federated Linear Learning with Certified Data Unlearning</title>
				<link>https://minghuichen.com/publication/preprint_2024_f2l2/</link>
				<pubDate>Sun, 24 May 2026 00:00:00 +0000</pubDate>
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				<description>&lt;p&gt;&lt;strong&gt;Authors:&lt;/strong&gt; Ruinan Jin, &lt;strong&gt;Minghui Chen&lt;/strong&gt;, Qiong Zhang, Xiaoxiao Li&lt;/p&gt;&#xA;&lt;p&gt;&lt;strong&gt;Published in:&lt;/strong&gt; &#xA;&lt;a href=&#34;https://doi.org/10.1109/TNNLS.2026.3683398&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;IEEE Transactions on Neural Networks and Learning Systems (&lt;strong&gt;IEEE TNNLS&lt;/strong&gt;, 2026)&lt;/a&gt;, Early Access, pp. 1–10&lt;/p&gt;&#xA;&lt;p&gt;&lt;strong&gt;Earlier workshop version:&lt;/strong&gt; &#xA;&lt;a href=&#34;https://arxiv.org/abs/2306.02216v1&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Forgettable Federated Linear Learning with Certified Data Removal&lt;/a&gt;, WWW 2024 FL@FM Workshop (&lt;strong&gt;Best Paper Award&lt;/strong&gt; 🏆)&lt;/p&gt;&#xA;&#xA;&#xA;&#xA;&#xA;&lt;h2 id=&#34;overview&#34;&gt;Overview&#xA;  &lt;a href=&#34;#overview&#34;&gt;&lt;svg class=&#34;anchor-symbol&#34; aria-hidden=&#34;true&#34; height=&#34;26&#34; width=&#34;26&#34; viewBox=&#34;0 0 22 22&#34; xmlns=&#34;http://www.w3.org/2000/svg&#34;&gt;&#xA;      &lt;path d=&#34;M0 0h24v24H0z&#34; fill=&#34;currentColor&#34;&gt;&lt;/path&gt;&#xA;      &lt;path d=&#34;M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76.0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71.0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71.0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76.0 5-2.24 5-5s-2.24-5-5-5z&#34;&gt;&lt;/path&gt;&#xA;    &lt;/svg&gt;&lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;p&gt;Forgettable Federated Linear Learning uses pre-trained models to approximate deep networks linearly during federated training. Its certified unlearning procedure lets the server remove a target client&amp;rsquo;s contribution without contacting clients again or retaining historical models. Experiments spanning small and large datasets, convolutional networks, and foundation models evaluate the balance between model accuracy and effective unlearning.&lt;/p&gt;</description>
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