Simply put, federated learning is a decentralised form of machine learning. Google first introduced it in 2016 in a paper titled, 'Communication Efficient Learning of Deep Networks from Decentralized Data, which provided the first definition of federated learning, along with another research paper on federated optimisation titled 'Federated Optimization: Distributed Machine Learning for On-Device Intelligence.' Then, in 2017, Google, in a blog post, 'Federated Learning: Collaborative Machine Learning without Centralized Training Data,' explained in detail the nuances of this technique.