An issue with artificial intelligence applications (AI) is that they typically require training using large-scale data sets. Such AI training poses a challenge when the data required is personal information. This article by Computer Weekly examines the legal challenges being launched against generative AI systems. However, an emerging privacy-enhancing technology, federated learning, is gaining traction due to its capability to train algorithms without exchanging raw data. The global federated learning market is expected to increase by 10.5% by 2032. This IAPP article explores the technology's privacy-preserving principles, challenges, and examples of real-world applications.
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