Hetero-FedIoT: A Rule-Based Interworking Architecture for Heterogeneous Federated IoT Networks

The rapid growth of the Federated Internet of Things ecosystem has introduced new challenges in achieving seamless connectivity and interoperability across heterogeneous IoT networks. The presence of heterogeneous platforms and protocols creates significant obstacles for effective communication among cross-silos federated IoT nodes. To tackle this challenge, we have developed heterogeneous federated Internet of Things (Hetero-FedIoT), an innovative rule-based interworking architecture enabling interoperability and seamless connectivity among heterogeneous federated IoT networks (oneM2M, OCF, and EdgeX). Hetero-FedIoT offers a two-faceted solution to address these challenges. First, it incorporates a rule-based interworking mechanism that fosters effective collaboration among Hetero-FedIoT networks. Additionally, it introduces a novel aggregation function capable of achieving accelerated convergence, effectively handling both system and statistical heterogeneity. By leveraging device proxies, Hetero-FedIoT enables interoperability among heterogeneous FedIoT networks by translating protocols from platform-native formats to a common format and vice versa. As a result, collaborative model training can be seamlessly conducted without the need to consider underlying frameworks. Additionally, the novel aggregation algorithm employed by Hetero-FedIoT empowers nodes to customize the complexity of local models according to their communication and computation capabilities. This is accomplished through the dynamic adjustment of hidden channel widths, ensuring that the overall performance of the global model remains unaffected. This groundbreaking Hetero-FedIoT architecture establishes a foundation for enhanced interoperability and optimal performance. Extensive evaluation of Hetero-FedIoT has demonstrated superior computational and communication efficiency over baseline schemes. The Hetero-FedIoT system revolutionizes decentralized training under heterogeneous conditions, fostering widespread adoption.