Edinburgh Napier University

  Federated learning (FL) researches attempt to alleviate the increasing difficulty of training machine learning models, when the training data is generated in a massively distributed way. The key idea behind these methods is moving the training to locations of data generation, and periodically collecting and redistributing the model updates. We present our approach for transforming the general training algorithm of FL into a peer-to-peer-like process. Our experiments on baseline image classification datasets show that omitting central coordination in FL is feasible.