Federated learning plays a crucial role in the future of AI by enhancing data privacy and security while allowing for collaborative improvements in AI models across decentralized networks. This technique enables devices to learn from local data without transmitting it, thus preserving sensitive information [1][3][6]. It is particularly beneficial in sectors like healthcare and finance, where data privacy is paramount [5][9]. The approach fosters diversity in data, resulting in more robust models that can adapt to various user needs without compromising individual data security [11][12].

What role does "Federated Learning" play in the future of AI?

Federated Learning in AI: How It Works, Benefits and Challenges | Splunk

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What role does "Federated Learning" play in the future of AI?

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