In this workshop, we will give a brief introduction to federated learning (FL), which is a machine learning concept that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. We will go through several challenges of FL including weights aggregation, non-iid data, and privacy preserving.