Scaffold: Stochastic Controlled Averaging for Federated Learning

Federated learning has emerged as an important paradigm in modern large-scale machine learning. Unlike in traditional centralized learning where models are trained using large datasets stored in a central server, in federated learning, the training data remains distributed over a large number of clients, which may be phones, network sensors, hospitals, or alternative local information sources.