Federated learning, akin to the SETI@home project's approach to distributed computing, allows AI models to be collaboratively trained across different devices while keeping the data localized. This method not only upholds privacy but also enhances the collective intelligence of the model.
Understanding Federated Learning
In this model, a central server dispatches an AI model to various devices. Each device trains the model with its unique dataset, then returns the updated model to the server. The server then synthesizes these updates to improve the overall AI model.
Exploring Federated Learning Initiatives
Nvidia FL Projects: These initiatives are leveraging the power of GPUs to streamline AI model training.
Intel FL Projects: Intel's efforts showcase how CPUs can be effectively utilized for sophisticated AI computations.
Healthcare FL Projects: In the healthcare sector, federated learning enables hospitals to collaborate on AI development while maintaining the confidentiality of patient data, thereby safeguarding privacy.
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