Federated-Learning
Federated learning (FL) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging their data samples. In simpler terms, this technique trains the ML model on the place where data resides. For more details check this link.
Advantages
FL helps in privacy preservation of the data. This technique mainly involves privacy preservation techniques such as Homomorphic Encryption, Secured Multi Party Computation (SMPC) and Differential Privacy.