Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Things
Shaowen Qin, Jianfeng Zeng, Haodong Guo, Xiaohuan Li, Jiawen Kang, Qian Chen, and Dusit Niyato

TL;DR
This paper introduces FEIBN, a federated evaluation framework for intent-based networking in IIoT, utilizing LLMs and a novel federated learning mechanism to improve policy verification efficiency and privacy.
Contribution
It proposes FEIBN, integrating LLMs with federated learning and SSAFL to enhance distributed policy verification in IIoT environments.
Findings
SSAFL improves model accuracy and convergence speed.
Reduces communication cost by 27.8%.
Enhances privacy-preserving policy verification.
Abstract
Intent-Based Networking (IBN) offers a promising paradigm for intelligent and automated network control in Industrial Internet of Things (IIoT) environments by translating high-level user intents into executable network strategies. However, frequent strategy deployment and rollback are impractical in real-world IIoT systems due to tightly coupled workflows and high downtime costs, while the heterogeneity and privacy constraints of IIoT nodes further complicate centralized policy verification. To address these challenges, we propose FEIBN, a Federated Evaluation Enhanced Intent-Based Networking framework. FEIBN leverages large language models (LLMs) to align multimodal user intents into structured strategy tuples and employs federated learning to perform distributed policy verification across IIoT nodes without exposing raw data. To improve training efficiency and reduce communication…
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Taxonomy
TopicsSoftware-Defined Networks and 5G · IoT and Edge/Fog Computing · Caching and Content Delivery
