From the course: Understanding Generative AI in Cloud Computing: Services and Use Cases (2023)

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Federated learning for distributed generative models on the cloud

Federated learning for distributed generative models on the cloud

From the course: Understanding Generative AI in Cloud Computing: Services and Use Cases (2023)

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Federated learning for distributed generative models on the cloud

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- [Narrator] One prominent feature of decentralized data ownership model lies in providing all remote devices and servers with the possession of their respective data, granting them complete overriding rights at all times. This serves both legal requirements and desirable business ethics aimed at putting owners back in the driver's seat. Federated learning offers an exciting approach to distributed learning because it enables parties or devices to powertrain an array of machine learning algorithms without allowing exposure to private information. In cloud-based settings, federated learning can offer several advantages when applied to distributed generative models. Privacy preservation. By storing delicate information on local storage systems instead of transmitting it to central servers for operating needs, such as training, this model ensures improved privacy. This approach is especially beneficial since particular…

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