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

TL;DR

This paper discusses methods to enhance trustworthiness in federated learning for Industrial IoT by improving interpretability and robustness, ensuring reliable and transparent decision-making in industrial environments.

Contribution

It introduces strategies to bridge the gap between interpretability and robustness in federated learning tailored for IIoT applications, addressing key trust and reliability issues.

Findings

Proposed design strategies for trustworthy FL in IIoT

Case studies demonstrating practical implementation

Insights into communication and decision-making in IIoT

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.