Instead of uploading your training data to improve a shared model, federated learning downloads the model to your device, trains it on your private data locally, then sends only the improved weights back. This separation means companies training AI on your behavior don't see your raw inputs, though they can still estimate what your data probably contains from how you changed the model.
Federated learning is a machine learning technique where AI models are trained across many devices without raw personal data ever leaving your phone or computer. Instead of sending your data to a central server, only model updates are shared, theoretically preserving your privacy.
Knowing how federated learning works helps you evaluate the real privacy claims made by apps like keyboards, health trackers, and messaging platforms that use on-device AI, and it reveals where genuine protections end and marketing language begins.
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