  Federated learning (FL) is a new artificial intelligence concept that enables
Internet-of-Things (IoT) devices to learn a collaborative model without sending
the raw data to centralized nodes for processing. Despite numerous advantages,
low computing resources at IoT devices and high communication costs for
exchanging model parameters make applications of FL in massive IoT networks
very limited. In this work, we develop a novel compression scheme for FL,
called high-compression federated learning (HCFL), for very large scale IoT
networks. HCFL can reduce the data load for FL processes without changing their
structure and hyperparameters. In this way, we not only can significantly
reduce communication costs, but also make intensive learning processes more
adaptable on low-computing resource IoT devices. Furthermore, we investigate a
relationship between the number of IoT devices and the convergence level of the
FL model and thereby better assess the quality of the FL process. We
demonstrate our HCFL scheme in both simulations and mathematical analyses. Our
proposed theoretical research can be used as a minimum level of satisfaction,
proving that the FL process can achieve good performance when a determined
configuration is met. Therefore, we show that HCFL is applicable in any
FL-integrated networks with numerous IoT devices.
