In Edge Learning, training data are non-independently and identically distributed (non-IID). Applying the same learning strategy for all workers fails to work efficiently. In this chapter, we introduce federated learning, where the training data are always non-IID due to data isolation. Then, we summarize enabling technologies for efficient training with non-IID data. We also propose a reinforcement learning-based method that takes non-IID property and resource-constrain into consideration and adjusts the hyper-parameters to accelerate the loss descent efficiency.