Decentralized Model Training

Topic

Decentralized model training, often implemented as federated or collaborative learning, is a machine learning technique where multiple distributed nodes or clients collaboratively train a shared model. Instead of consolidating training data into a single centralized repository, each node processes its own local data and only shares model updates or gradients, enhancing data privacy and security.