T-FedHA: A Trusted Hierarchical Asynchronous Federated Learning Framework for Internet of Things

Abstract

Federated Learning (FL) is a distributed machine learning system designed to effectively address potential data privacy concerns, making it particularly promising for the Internet of Things (IoT). However, challenges arise due to the trust and heterogeneity of IoT devices. FL often assumes devices are trustworthy and curious, which is impractical in the realistic IoT environment where the trustworthiness of participating devices becomes an intractable problem. Additionally, the diverse computational power and communication efficiency across IoT devices impact the efficiency and performance of FL. To address these challenges and enable the effective implementation of FL in the IoT environment, this article proposes a Trusted Hierarchical Asynchronous Federated Learning Framework (T-FedHA). The framework introduces a “Device-Edge-Central” hierarchy and employs a distributed trust management model. This model manages the trust of IoT devices, selecting devices that meet trust requirements for FL training. This enhances the security and trustworthiness of the overall FL system. Furthermore, T-FedHA integrates standard FL methods with a hierarchical asynchronous strategy that includes edge servers. This approach improves model aggregation and update processes, employing an asynchronous technique that considers the staleness of model updates to enhance overall performance and effectiveness. Extensive experiments confirm the efficacy of T-FedHA. The distributed trust management model efficiently calculates and manages IoT device trust, allowing only trusted devices to participate in FL training. The hierarchical asynchronous approach proves more feasible and efficient, as well as robust and flexible, contributing to the reliability of the proposed framework.