Personalized and sustainable federated learning in integrated Internet of Things

Abstract

Integrated Internet of Things (IoT) brings novel opportunities for pervasive smart services, as these systems allow for seamless information and resource sharing among IoT devices. Meanwhile, Federated Learning (FL) has emerged as a new framework for distributed deployment of machine learning models and a promising approach for implementing intelligent IoT systems. However, integrated IoT systems are usually composed of diverse IoT devices from different systems, and thus their ownership, roles, data distribution, and capabilities are heterogeneous. Current FL algorithms mainly focus on handling non-Independent and Identically Distributed (Non-IID) issues, but often result in reduced and unsustainable performance in integrated IoT systems. Therefore, we investigate in this paper the problem of personalized and sustainable FL in integrated IoT systems. First, we argue that different parties in integrated IoT are heterogeneous and limited in available resources for FL, and these parties are also selfish and expect rational outcomes during cooperation, which is essential for guaranteeing the sustainability of integrated IoT. Then, this paper provides a novel framework for device selection in FL. It first sets one instance of the model for each device, and iteratively selects devices to participate in model training based on the joint consideration of local model accuracy, similarity of parameters, and remaining resources per device. The proposed method guarantees the rational allocation of resources to maintain balanced model performance across all devices. In this way, the sustainability of the whole IoT system is improved such that no devices will suffer extreme resource exhaustion or poor performance. Finally, extensive evaluation is conducted to validate the advanced performance of the proposed method in integrated IoT systems.