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	<title>secure multi-party computation &#8211; BIOENGINEER.ORG</title>
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		<title>Encrypted AI Training Gets a Speed Boost Without Sacrificing Privacy</title>
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				<category><![CDATA[Technology]]></category>
		<category><![CDATA[adaptive scheduling]]></category>
		<category><![CDATA[data privacy]]></category>
		<category><![CDATA[data protection]]></category>
		<category><![CDATA[encrypted training]]></category>
		<category><![CDATA[federated learning]]></category>
		<category><![CDATA[gradient sparsification]]></category>
		<category><![CDATA[Homomorphic Encryption]]></category>
		<category><![CDATA[privacy-preserving machine learning]]></category>
		<category><![CDATA[ResNet]]></category>
		<category><![CDATA[secure multi-party computation]]></category>
		<category><![CDATA[Shamir secret sharing]]></category>
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					<description><![CDATA[Researchers have unveiled a collaborative encryption framework that trains deep learning models on sensitive data up to three times faster than existing homomorphic methods while keeping accuracy losses to just 1.2 percent.]]></description>
		
		
		
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