## 1 Introduction  
The Industrial Internet of Things (IIoT) represents a transformative shift in industrial operations, enabling enhanced connectivity and automation across diverse applications [1]. Central to IIoT are digital twins--virtual replicas of physical assets--that facilitate real-time monitoring, predictive maintenance, and operational optimization [2]. Digital twins bridge the physical and digital realms through synchronized data flows, creating opportunities for smarter decision-making, reduced downtime, and increased productivity [3]. The three integral components of digital twins--physical assets, their virtual counterparts, and the IoT-enabled linkage between them--serve as the foundation for IIoT systems [4]. Incorporating AI-driven analytics into digital twins has expanded their capabilities but also introduced challenges related to data security, privacy, and the explainability of automated decisions [5]. Addressing these challenges necessitates a robust framework that integrates federated learning (FL), blockchain technology, and Explainable AI (XAI) to create a secure, scalable, and interpretable digital twin ecosystem. FL has emerged as a promising approach for distributed machine learning, enabling the training of models directly on edge devices where data are generated [6]. This decentralized paradigm enhances privacy by eliminating the need for raw data to leave local deviceswhile also improving model adaptability by leveraging diverse datasets [7]. Figure 1 illustrates the distributed training concept within an IIoT context, demonstrating how edge devices contribute to a global model by processing localized data securely. While this figure focuses on autonomous vehicles, the underlying principles apply broadly across the IIoT spectrum, encompassing diverse sectors, such as manufacturing, energy, and healthcare. Despite its advantages, FL faces challenges in ensuring data integrity and trustworthiness. Blockchain technology addresses these limitations by providing a decentralized, immutable ledger for recording data transactions and model updates [8; 9]. By automating governance through smart contracts, blockchain ensures data authenticity, compliance, and secure access across IIoT assets [10; 11; 12]. These capabilities are vital for fostering trust in data-driven decision-making within industrial environments. Equally important is the integration of XAI to overcome the \"black-box\" nature of AI models. XAI techniques enhance the interpretability of machine learning outputs, offering insights into decision-making processes. This transparency builds stakeholder trust and ensures compliance with regulatory standards, particularly in high-stakes industrial applications [13]. Combined with self-optimization capabilities, where systems autonomously adapt to changing conditions, XAI strengthens the utility of digital twins in dynamic IIoT settings [14]. This paper presents a holistic framework that synergizes FL, blockchain, and XAI to address security, privacy, and interpretability challenges in IIoT. The contributions of this work are as follows:  
* Introducing blockchain for secure, transparent, and decentralized data management within IIoT ecosystems. * Leveraging FL to enable privacy-preserving, decentralized training of machine learning models on edge devices. * Employing XAI to enhance the interpretability of AI-driven decisions, fostering trust among stakeholders. Figure 1: An illustration of the concept of distributed training for machine learning models in the context of autonomous vehicles. * Providing a scalable and resilient framework for operational efficiency and real-time decision-making in IIoT. The integration of these technologies offers a transformative approach to managing digital twins, driving secure, efficient, and trustworthy operations in IIoT environments. The rest of this paper is structured as follows: Section 2 reviews related work, examining how blockchain integration with digital twins has enhanced various sectors, such as construction and predictive maintenance.