HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks
This work addresses communication efficiency for federated learning in very large scale IoT networks, representing an incremental improvement with a novel compression method.
The paper tackles the problem of high communication costs and limited computing resources in federated learning for large-scale IoT networks by proposing a novel compression scheme called HCFL, which reduces data load without altering the FL structure, demonstrating applicability in simulations and analyses.
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.