Federated learning respects data boundaries by processing sensitive information on your device before sharing it, which matters if you're dealing with medical records, financial data, or other information you genuinely want to keep local. But your device becomes the weak point—a compromised phone can leak the very data federated learning was supposed to protect, so it's one layer of privacy strategy, not a complete solution.
Federated learning is a machine learning approach where AI models are trained across many devices without the raw data ever leaving those devices, meaning the model learns from your behavior locally rather than sending your information to a central server.
Knowing how federated learning works helps you evaluate privacy claims made by apps and platforms, and understand the real boundaries between what AI learns about you and what data it actually collects and stores remotely.
Peri can explain this concept, give practical examples, help you decide whether it applies to your situation, or recommend a journey if appropriate.
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