Democratizing Federated Learning
What is Federated Learning? Federated learning (FL) is a machine learning (ML) mechanism where different parties participate in a machine learning task and build a global model without sharing training data with any other parties. While there are several different training modes, a typical setting consists of two types of computing nodes: (1) trainer and (2) aggregator. The trainer node processes a dataset locally to build a model; and a set of trainer nodes share their model parameters with the aggregator node.