Abstract: A system for training a model using federated learning is provided. The system includes a server, and a plurality of vehicles. Each of the vehicles includes a controller programmed to: transmit first knowledge data including information about a plurality of feature vectors and information about a plurality of predictions, receive first aggregated knowledge from the server, and train a local model based on the first aggregated knowledge. The server averages the first knowledge data received from the plurality of vehicles to generate the aggregated knowledge.

Abstract: A system for training a model for image processing using federated learning is provided. The system includes a controller programmed to obtain information about a computation resource in each of a plurality of edge nodes, assign training steps to the plurality of edge nodes based on the information about the computation resource, determine frequencies of uploading local model parameters for the plurality of edge nodes based on the assigned training steps, receive local model parameters from one or more of the plurality of edge nodes based on the determined frequencies, and update a global model based on the received local model parameters.