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		<title>Federated Learning Emerges as a Stronger Shield Against Membership Inference Attacks</title>
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		<pubDate>Mon, 19 Oct 2026 17:00:08 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[biometric authentication]]></category>
		<category><![CDATA[black-box evaluation]]></category>
		<category><![CDATA[data privacy]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[dropout]]></category>
		<category><![CDATA[federated learning]]></category>
		<category><![CDATA[loss surface geometry]]></category>
		<category><![CDATA[Medical Imaging]]></category>
		<category><![CDATA[membership inference attack]]></category>
		<category><![CDATA[privacy auditing]]></category>
		<category><![CDATA[semantic segmentation]]></category>
		<category><![CDATA[sharp minima]]></category>
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					<description><![CDATA[New research from IIT Kharagpur shows that federated learning drives models toward flatter loss minima that suppress memorization, and introduces a lightweight black-box tool for measuring membership privacy risk.]]></description>
		
		
		
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