  Federated learning is an emerging distributed machine learning framework for
privacy preservation. However, models trained in federated learning usually
have worse performance than those trained in the standard centralized learning
mode, especially when the training data are not independent and identically
distributed (Non-IID) on the local devices. In this survey, we pro-vide a
detailed analysis of the influence of Non-IID data on both parametric and
non-parametric machine learning models in both horizontal and vertical
federated learning. In addition, cur-rent research work on handling challenges
of Non-IID data in federated learning are reviewed, and both advantages and
disadvantages of these approaches are discussed. Finally, we suggest several
future research directions before concluding the paper.
