Introduction to Federated Learning

There are over 5 billion mobile device users all over the world. Such users generate massive amounts of data--via cameras, microphones, and other sensors like accelerometers--which can, in turn, be used for building intelligent applications. Such data is then collected in data centers for training machine/deep learning models in order to build intelligent applications. However, due to data privacy concerns and bandwidth limitations, common centralized learning techniques aren't appropriate--users are much less likely to share data, and thus the data will be only available on the devices. This is where federated learning comes into play. According to Google's research paper titled, Communication-Efficient Learning of Deep Networks from Decentralized Data [1], the researchers provide the following high-level definition of federated learning: A learning technique that allows users to collectively reap the benefits of shared models trained from [this] rich data, without the need to centrally store it.