Federated Machine Learning - Collaborative Machine Learning without Centralised Training Data

Federated machine learning was developed by McMahan et al who principally developed it for mobile devices such as cell phones to train a local model. These local models are aggregated centrally into a final central model. The original paper produced by McMahan presented a number of experiments on image classification, and language models where they used 2000 individual local models each were generated with small amounts of data. The image classification task they undertook was CIFAR-10 image classification challenge, and the language model task produced an LM of Shakespear's plays. The image classification task gained an accuracy of 99% using a Convolutional Neural Network in a Federated strategy.