用联邦学习提升工业物联网意图网络的评估效率，减少通信开销。
Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Things
- 基于策略相似性选择参与节点，异步上传更新，降低通信负担。
- 相比基线方法，模型准确率更高，收敛速度更快，通信成本更低。
- 适合资源异构、隐私要求高的工业物联网场景使用。
意图网络（IBN）为工业物联网（IIoT）环境中的智能自动化网络控制提供了新范式，通过将用户高层意图转化为可执行的网络策略。然而，频繁的策略部署与回滚因工作流紧耦合及高停机成本而不切实际，同时节点异构性和隐私约束使得集中式策略评估更加复杂。为此，我们提出联邦评估增强的意图网络框架（FEIBN），利用大语言模型（LLMs）将用户意图转化为结构化策略元组，并采用联邦学习实现分布式策略评估。为提升训练效率并减少通信开销，设计了策略相似性感知的联邦学习机制（SSAFL），根据策略相似性和资源状态选择相关节点，并仅在本地更新显著时触发异步模型上传。实验表明，该方法在模型准确率、收敛速度和通信成本方面均优于基线。
原文摘要 · Abstract (English)
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 due to tightly coupled workflows and high downtime costs, while node heterogeneity and privacy constraints further complicate centralized strategy evaluation. To address these challenges, we propose a Federated Evaluation Enhanced Intent-Based Networking framework (FEIBN), which leverages large language models (LLMs) to translate user intents into structured strategy tuples and employs federated learning to support distributed strategy evaluation. To improve training efficiency and reduce communication overhead, we design a Strategy Similarity Aware Federated Learning mechanism (SSAFL), which selects nodes relevant to the task based on strategy similarity and resource status, and triggers asynchronous model uploads only when local updates are significant. Experiments demonstrate that the proposed method improves model accuracy, accelerates convergence, and reduces communication cost compared with the baselines.
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