Show Notes
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Description: Imagine you want to train a smart computer program (we call this AI) using lots of information, like data from many different devices or places.
The usual way to do this is to gather all that information and put it into one big central computer. But if that information is private, like your personal photos, health records, or what you type on your phone, putting it all in one big place can be risky! If that central place gets hacked, everyone's private information could be stolen.
Federated Learning is a different, smarter way to train AI. Instead of sending your private information to a central place, the AI program is sent to where the data already is – like onto your phone, your computer, or the computer at a hospital.
The program learns from the data directly on that local device. The important part is that what gets shared back is not your private data itself, but only the changes or "updates" that the program made to get better. These updates from many different devices or places are then combined to make the main AI program smarter for everyone.
Why is this good?
It helps keep your private information much safer and more secure. Since your actual data never leaves your local device or organization, it greatly reduces the chance of it being seen by the wrong people or stolen in a data breach. This approach is especially useful for training AI in areas with very sensitive information, like allowing hospitals to train AI using patient data without ever having to share the actual medical records.
It's also used to train AI to find computer threats while keeping sensitive company information private. Using Federated Learning also helps companies follow rules about keeping data private, like GDPR
More from Marlon Bonajos | Ai Revolution Movement Show
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