Federated learning at the edge in Industrial Internet of Things: A review

• Published in: Sustainable computing informatics and systems Vol. 46; p. 101087
• Main Authors: sah, Dinesh kumar, Vahabi, Maryam, Fotouhi, Hossein
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.06.2025
• Subjects: Adversarial machine learning; Anomaly detection; Anomaly detection (AD); Contrastive Learning; Differential privacy; Edge computing; Edge computing (EC); Federated learning; Federated learning (FL); Industrial internet of thing; Industrial Internet of Things (IIoT); Machine learning; Machine learning (ML); Privacy preservation; Privacy preservation (PP); Anomaly detection (AD); Federated learning (FL); Privacy preservation (PP); Machine learning (ML); Edge computing (EC); Industrial Internet of Things (IIoT)
• ISSN: 2210-5379; 2210-5387
• DOI: 10.1016/j.suscom.2025.101087

The convergence of Federated learning (FL) and Edge computing (EC) has emerged as an essential paradigm, particularly within the Industrial Internet of Things (IIoT) to enable the intelligent decision making. This work diligently examines the current state-of-the-art research at the intersection of FL, EC, and IIoT. An extensive review of the literature explores the diverse applications and challenges associated with this integration. The challenges range from privacy preservation and communication overhead to resource allocation. The incorporation of edge devices at which ensuring the federated learning in distributed manner helps to minimize energy consumption in IIoT, ultimately leads to a sustainable computing environment. By exploring the existing literature and research advancements, our goal is to highlight existing Edge-IoT software and hardware platforms and assess their usability in addressing challenges. In addition, we review existing recent frameworks, methodologies, and models employed to address these challenges, focusing on key performance matrices and its domain such as application, networking, and learning. We highlight the achievements and potential of FL and EC and underscore the need for tailored solutions to suit the unique demands of IIoT. Furthermore, we identify some of the major challenges as opportunities for future research, requires interdisciplinary collaboration and innovative algorithmic solutions. This work can help navigate through the challenges and unlock the full potential, contributing to the advancement of future IIoT applications. •Emerging Paradigm: The convergence of Federated learning (FL) and Edge computing (EC) is becoming crucial, especially within the Industrial Internet of Things (IIoT).•Challenges and Solutions: The paper reviews literature on the integration of Federated learning (FL) and Edge computing (EC) in IIoT, identifying key challenges such as privacy preservation, communication overhead, and the need for real-time decision-making.•Benefits of FL and EC: It offer significant advantages in IIoT by enabling decentralized model training, reducing energy consumption, latency, enhancing privacy, and mitigating bandwidth constraints.