The integration of Federated Learning (FL) and Graph Neural Networks (GNNs) has emerged as a promising paradigm for distributed intelligence in Internet of Things (IoT) environments. However, challenges related to trust, device heterogeneity, and energy efficiency continue to hinder scalable deployment in real-world settings. This paper presents Trust-FedGNN, a trust-aware federated graph learning framework that jointly addresses reliability, robustness, and sustainability in IoT ecosystems. The framework combines reliability-based reputation modeling, energy-aware client scheduling, and dynamic graph pruning to reduce communication overhead and energy consumption during collaborative training, while mitigating the influence of unreliable or malicious participants. Trust evaluation is explicitly decoupled from energy availability, ensuring that honest but resource-constrained devices are not penalized during aggregation. Experimental results on benchmark IoT datasets demonstrate up to 5.8% higher accuracy, 3.1% higher F1-score, and approximately 22% lower energy consumption compared with State-of-the-Art federated baselines, while maintaining robustness under partial adversarial participation. These results confirm the effectiveness of Trust-FedGNN as a secure, robust, and energy-efficient federated graph learning solution for heterogeneous IoT networks (a proof-of-concept evaluation across 10 federated clients).
Mariana Reis (2026) studied this question.