Federated learning is proposed as a machine learning setting to enable distributed edge
devices, such as mobile phones, to collaboratively learn a shared prediction model while
keeping all the training data on device, which can not only take full advantage of data
distributed across millions of nodes to train a good model but also protect data privacy.
However, learning in scenario above poses new challenges. In fact, data across a massive
number of unreliable devices is likely to be non-IID (identically and independently …