Federated Machine Learning

Federated machine learning is a machine learning setting where multiple entities (clients) collaborate in solving a machine learning problem, while keeping the data decentralized. This setting is particularly useful when the data is sensitive and cannot be shared due to privacy concerns. Federated machine learning algorithms allow the clients to train a global model without sharing their data with each other or with a central server. This approach is used in various applications, such as healthcare, finance, and IoT, where data privacy is a major concern.