Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Things

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 overhead, we design SSAFL, a Strategy Similarity Aware Federated Learning mechanism that selects task-relevant nodes based on strategy similarity and resource status, and triggers asynchronous model uploads only when updates are significant. Experiments demonstrate that SSAFL can improve model accuracy, accelerate model convergence, and reduce the cost by 27.8% compared with SemiAsyn.