SYSTEM AND METHODS FOR COLLABORATIVE FEDERATED LEARNING

原文（中国語）

A system for decentralized machine learning may include a hub node in communication with a plurality of spoke nodes. The hub node may receive, from a spoke node, trained model parameters for a machine learning model. The hub node aggregate the received model parameters. The hub node may exchange the aggregated model parameters with another hub node via gossip-based communication creating updated model parameters. The hub node may transmit the updated model parameters to the spoke nodes. A spoke node may receive the updated model parameters from the hub node and model parameters from a second hub node. The spoke node may aggregate the updated model parameters with the model parameters from the second hub node. The spoke node may train the local model parameters using training data.