Simulating Broadband Analog Aggregation for Federated Learning

Abstract: With increasing amounts of data coming fromconnecting progressively more devices, new machine learningmodels have risen. For wireless networks the idea of using adistributed approach to machine learning has gained increasingpopularity, where all nodes in the network participate in creatinga global machine learning model by training with the localdata stored at each node, an example of this approach is calledfederated learning. However, traditional communication protocolshave been proven inefficient. This opens up opportunities todesign new machine-learning specific communication schemes.The concept ofOver-the-air computationis built on the fact thata wireless communication channel can naturally compute somelinear functions, for instance the sum. If all nodes in a networktransmits simultaneously to a server, the signals are aggregatedbefore reaching the server.