Federated learning (FL) is a form of privacy-preserving technology in which an algorithm is trained across heterogeneous datasets. By training the model across multiple entities or servers holding local data samples, FL removes the need to exchange underlying data, allowing firms to achieve the benefits of a collaborative and networked approach, without the risk of sharing sensitive information…
