Methods and systems for horizontal federated learning using non-iid data

Abstract

Methods and systems for horizontal federated learning are described. A plurality of sets of local model parameters is obtained. Each set of local model parameters was learned at a respective client. For each given set of local model parameters, collaboration coefficients are computed, representing a similarity between the given set of local model parameters and each other set of local model parameters. Updating of the sets of local model parameters is performed, to obtain sets of updated local model parameters. Each given set of local model parameters is updated using a weighted aggregation of the other sets of local model parameters, where the weighted aggregation is computed using the collaboration coefficients. The sets of updated local model parameters are provided to each respective client.