Federated learning

Federated learning is a machine learning technique that enables multiple decentralized devices or servers to collaboratively train a model while keeping the data localized. This method enhances privacy and security by ensuring that raw data never leaves the device, with only model updates being shared with a central server. Federated learning is particularly beneficial for applications in healthcare, finance, and mobile applications where data privacy is paramount.