Federated learning vector quantization for dealing with drift between nodes

Federated learning is an efficient methodology to reduce the data transmissions to the server when working with large amounts of (sen- sor) data from diverse physical locations. When using data from different sensor devices concept drift between the single sensors poses an additional challenge. In this contribution we define a formal framework for federated learning with concept drift and propose a version of federated LVQ dealing with concept drift induced by different hyperspectral cameras. We evalu- ate this approach experimentally and demonstrate its robustness to class imbalance and missing classes.