Enhanced Collaborative Edge Intelligence for Explainable and Transferable Image Recognition in 6G-Aided IIoT

TL;DR

This paper introduces IRCE, a new method for image recognition in 6G-aided IIoT that improves explainability and collaboration between edge servers.

Contribution

IRCE introduces an explainable layer and LMMD loss to enhance explainability and transferability in 6G-aided IIoT image recognition.

Findings

IRCE provides visual prototypes to explain image recognition decisions, increasing transparency.

The LMMD loss enables effective domain adaptation and collaboration across distributed edge servers.

Simulations show IRCE outperforms traditional methods in accuracy, adaptability, and efficiency.

Abstract

The Industrial Internet of Things (IIoT) has revolutionized industry through interconnected devices and intelligent applications. Leveraging the advancements in sixth-generation cellular networks (6G), the 6G-aided IIoT has demonstrated a superior performance across applications requiring low latency and high reliability, with image recognition being among the most pivotal. However, the existing algorithms often neglect the explainability of image recognition processes and fail to address the collaborative potential between edge computing servers. This paper proposes a novel method, IRCE (Intelligent Recognition with Collaborative Edges), designed to enhance the explainability and transferability in 6G-aided IIoT image recognition. By incorporating an explainable layer into the feature extraction network, IRCE provides visual prototypes that elucidate decision-making processes, fostering greater transparency and trust in the system. Furthermore, the integration of the local maximum mean discrepancy (LMMD) loss facilitates seamless transfer learning across geographically distributed edge servers, enabling effective domain adaptation and collaborative intelligence. IRCE leverages edge intelligence to optimize real-time performance while reducing computational costs and enhancing scalability. Extensive simulations demonstrate the superior accuracy, explainability, and adaptability of IRCE compared to those of the traditional methods. Moreover, its ability to operate efficiently in diverse environments highlights its potential for critical industrial applications such as smart manufacturing, remote diagnostics, and intelligent transportation systems. The proposed approach represents a significant step forward in achieving scalable, explainable, and transferable AI solutions for IIoT ecosystems.

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