What if multiple factory sites using AI shared their model insights, enabling learnings from similar processes at different locations to be integrated into a federated learning model?

Manufacturing facilities often operate in isolation, especially when using edge AI. Each factory trains its models locally, keeping sensitive data private, but missing out on the opportunity for shared improvements and advancements.

Imagine a network where factories with similar processes, can collaborate through a shared AI model.

Here's how it works:

A model is initially developed in a lab and deployed to each factory, allowing them to locally train the model with their unique data.

While sensitive data remains private, the essence of what each factory learns—the parameter updates—is shared.

Instead of sharing sensitive data or trade secrets, each facility exchanges only the learnings derived from its local data. These parameters, representing the insights gained, are combined to form an improved model.

This process enables collaborative advancement while maintaining privacy, as no raw data or proprietary information is shared between factories.

By connecting factories in this AI network, each site benefits from a continuously evolving model that reflects shared learnings from all participating factories. The improved model is then sent back to each factory, bringing in collective advancements and enhancing the overall performance and accuracy of processes across the board.This approach ensures that factories can work together to refine AI capabilities, boosting efficiency and productivity across locations.

Privacy is preserved, trade secrets remain secure, and each factory gains access to a more robust and intelligent model.

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