Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence

TLDR

Industrial AI relies on deep learning but centralized training raises privacy concerns, and federated learning, while promising, can still be vulnerable to parameter‑based attacks on sensitive industrial data. This work proposes an efficient, privacy‑enhanced federated learning (PEFL) scheme tailored for industrial AI. PEFL is a noninteractive protocol that securely aggregates local model updates, preventing leakage even when multiple entities collude. Experiments on real‑world data show PEFL outperforms existing solutions in accuracy and efficiency while safeguarding private data.

Abstract

By leveraging deep learning-based technologies, industrial artificial intelligence (IAI) has been applied to solve various industrial challenging problems in Industry 4.0. However, for privacy reasons, traditional centralized training may be unsuitable for sensitive data-driven industrial scenarios, such as healthcare and autopilot. Recently, federated learning has received widespread attention, since it enables participants to collaboratively learn a shared model without revealing their local data. However, studies have shown that, by exploiting the shared parameters adversaries can still compromise industrial applications such as auto-driving navigation systems, medical data in wearable devices, and industrial robots' decision making. In this article, to solve this problem, we propose an efficient and privacy-enhanced federated learning (PEFL) scheme for IAI. Compared with existing solutions, PEFL is noninteractive, and can prevent private data from being leaked even if multiple entities collude with each other. Moreover, extensive experiments with real-world data demonstrate the superiority of PEFL in terms of accuracy and efficiency.