Enabling Trustworthy Federated Learning in Industrial IoT: Bridging the Gap Between Interpretability and Robustness

TL;DR

This paper addresses the challenge of enabling trustworthy Federated Learning in Industrial IoT by balancing interpretability and robustness. It surveys enhancement techniques, interpretable AI methods, and robustness strategies, and then presents design methodologies spanning deterministic, probabilistic, and adaptive-hybrid approaches tailored for harsh IIoT environments. Four case studies (smart manufacturing, energy, supply chain, and environmental monitoring) illustrate practical insights and roadmaps for deploying trustworthy FL in real-world industrial settings. The work highlights the importance of transparent, resilient FL systems to support privacy, security, and reliable decision-making in critical infrastructure contexts.

Abstract

Federated Learning (FL) represents a paradigm shift in machine learning, allowing collaborative model training while keeping data localized. This approach is particularly pertinent in the Industrial Internet of Things (IIoT) context, where data privacy, security, and efficient utilization of distributed resources are paramount. The essence of FL in IIoT lies in its ability to learn from diverse, distributed data sources without requiring central data storage, thus enhancing privacy and reducing communication overheads. However, despite its potential, several challenges impede the widespread adoption of FL in IIoT, notably in ensuring interpretability and robustness. This article focuses on enabling trustworthy FL in IIoT by bridging the gap between interpretability and robustness, which is crucial for enhancing trust, improving decision-making, and ensuring compliance with regulations. Moreover, the design strategies summarized in this article ensure that FL systems in IIoT are transparent and reliable, vital in industrial settings where decisions have significant safety and economic impacts. The case studies in the IIoT environment driven by trustworthy FL models are provided, wherein the practical insights of trustworthy communications between IIoT systems and their end users are highlighted.

Figures (2)

• Figure 1: The Federated Learning Architecture in an Industrial Environment with IIoT-enabled devices and XAI frameworks.
• Figure 2: The Dynamic Industrial Management through Personalized IIoT Framework driven by Federated Learning Models.