ZTE-FL: Dual-layer trust aggregation for secure federated learning in IIoT

Federated learning (FL) is increasingly used in Industrial Internet of Things (IIoT) systems to support collaborative intelligence while preserving data privacy. However, adversarial clients and implicit trust assumptions can undermine reliability in safety-critical environments. Existing defense methods mainly rely on statistical or behavioral trust metrics, which often fail to detect semantically harmful yet statistically consistent updates, particularly under poisoning and backdoor attacks. To address these limitations, we propose ZTE-FL, a zero-trust federated learning framework that combines behavioral and semantic trust during aggregation. Behavioral trust captures temporal consistency and gradient alignment, while semantic trust evaluates functional correctness using clean and trigger-aware validation datasets. By enforcing continuous verification, ZTE-FL reduces reliance on implicit trust at both client and server levels. Across the evaluated datasets, attack scenarios, and model architectures, experimental results show that ZTE-FL achieved an average global accuracy of 97.92%, outperforming BC+FL+Trust (97.17%) and FedTrust (96.89%) across 18 attack-model combinations. ZTE-FL also achieved an average resilience score of 96.91% and reduced communication cost by 20.21% compared with TAIM. These results demonstrate that ZTE-FL improves robustness against poisoning and backdoor attacks while maintaining efficiency in latency-sensitive IIoT environments.