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In the evolving landscape of digital advertising, federated learning offers a promising solution for privacy-preserving adtargeting. Ad tech companies can use it to create effective ad-targeting models. Ad tech companies face the challenge of balancing effective adtargeting with user privacy.
This technique is particularly useful in environments where data privacy is paramount, such as in personalized advertising and healthcare. Moreover, federated learning can improve the efficiency of adtargeting algorithms by enabling them to learn from a broader range of data inputs without compromising on privacy.
HE is particularly useful in scenarios where data privacy is paramount, such as in healthcare and finance. FL is particularly useful in industries where data privacy is critical, such as healthcare, finance, and advertising. Companies collect vast amounts of user data to deliver personalized ads.
Though there are currently sector-specific privacy laws, such as HIPAA for the healthcare industry and the Gramm-Leach-Bliley Act (GLBA) in financial services, the US has yet to enact a national data privacy framework that would be applicable to most businesses.
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