👉 Federation math is a framework that enables multiple machine learning models, often trained on decentralized data sources, to collaborate and make collective decisions while preserving data privacy. Each participant (or "node") in the federation learns a local model on their own private dataset, then shares only the model updates (e.g., gradients) with a central server. The server aggregates these updates using federated averaging to generate a global model, which is then sent back to the nodes for further personalization. This process ensures data remains localized, enhancing privacy, while leveraging diverse data across participants to improve model accuracy and robustness. It’s particularly valuable in scenarios like healthcare or finance, where data sharing is restricted but collective intelligence is critical.
