Federated Learning

Federated Learning allows AI models to be trained across multiple decentralized devices or servers holding local data, without the data ever leaving its source. Only model updates (like new parameters or weights) are shared and aggregated, preserving data privacy. Imagine training a chef across many restaurants, where each chef learns from their local ingredients and only shares general cooking technique improvements, not the actual food.