Federated learning is a decentralized machine-learning approach where models are trained across multiple devices or data silos without actually sharing any raw data. Each model being trained works locally on its own dataset. Only model parameters are aggregated centrally. This preserves data privacy, reduces the need for communication, and enables quicker training in sectors like healthcare and finance where data cannot leave its source.
