A Privacy-Preserving and Trustworthy Inference Framework for LLM-IoT Integration via Hierarchical Federated Collaborative Computing

This paper addresses the challenges of privacy protection, device constraints, resource heterogeneity, and trusted inference in the integration of large language models (LLMs) with Internet of Things (IoT) devices, proposing a Hierarchical Federated Collaborative Computing (HFCC) framework. Unlike traditional federated learning, HFCC employs horizontal splitting + chunking to reduce LLMs computation overhead. The framework dynamically splits LLMs into: 1) global shared layers optimized by edge servers, and 2) device-local layers trained on private data, ensuring raw data remains on-device. During inference, chunking of shared layers and dynamic task allocation adjust computational loads based on real-time device states, mitigating high-load security risks. Furthermore, leveraging the hierarchical federated learning architecture, the system employs an anonymized parameter aggregation mechanism during training to achieve multi-level privacy protection. Simultaneously, a cross-device consensus verification mechanism performs trusted validation of distributed intermediate results, effectively identifying malicious node behavior. Experiments show 58% faster inference in resource-constrained environments, significantly reduced data exposure risks, and 94% malicious node detection accuracy versus traditional federated learning. This lays a solid foundation for building an intelligent, efficient, and secure IoT ecosystem.