Federated Learning
A decentralized training approach where a shared global model is collaboratively learned across many distributed devices without exchanging raw, sensitive data.
Think of It Like This
Like a group of chefs perfecting a recipe by practicing in their own kitchens and only sharing their notes, rather than bringing all their ingredients together.
Federated learning is widely used to train models on mobile phones or in hospital networks where data privacy regulations strictly prohibit centralized data collection. Devices compute model updates locally and send only the aggregated gradients to a central server. This approach effectively preserves user privacy while still leveraging massive amounts of distributed data.