Explainable and Privacy‐Aware Collaborative Optimization for Efficient and Robust IIoT Clusters

Industrial Internet of Things (IIoT) networks face the critical challenge of balancing data privacy protection with network efficiency in multidevice, multinode environments. Existing methods often address privacy or efficiency issues in isolation, leading to limitations such as high computational overhead, poor robustness, and insufficient explainability in large‐scale deployments. To address these challenges, this paper proposes a collaborative optimization method that integrates Explainable Artificial Intelligence (XAI) with data privacy protection. The core of the proposed method includes: designing an XAI‐guided privacy protection model to enhance transparency, constructing a collaborative optimization framework for privacy and scheduling, and employing reinforcement learning for dynamic resource scheduling. Experimental results show that the proposed method achieves a latency of 15.2 ms and a bandwidth utilization of 92.3%, outperforming baseline methods such as Deep Q‐Network (DQN) and Shortest Job First (SJF), while also reducing privacy risk penalty (0.48) and improving system robustness. This study offers a novel approach for efficient and secure management of IIoT networks and provides a reference for privacy‐aware network optimization.