Modern industrial systems are increasingly defined by geographically distributed production lines, stringent privacy constraints, particularly to protect intellectual property and manufacturing process details, and heterogeneous data pipelines. In such environments, centralized Anomaly Detection (AD) is often impractical due to data governance restrictions and limited computational resources at local sites. To address these challenges, we propose a modular and lightweight AD framework based on diffusion models, named D-ADDA (Distributed Anomaly Detection based on Data Augmentation), designed for distributed deployment. Unlike many state-of-the-art methods that depend on large pre-trained models or external datasets, our approach is trained entirely on defective data locally available, enhancing privacy and domain specificity. A novel Data Augmentation Module (DAM) generates diverse defective samples through a multi-stage pipeline, which are used to train an attention-based diffusion model for defect synthesis. This architecture supports dislocated components across multiple clients, enabling training and inference in resource-constrained or privacy-sensitive settings. Experimental results on the MVTec AD dataset confirm the effectiveness of our approach, achieving an average classification accuracy of 60.46% across 14 categories, outperforming state-of-the-art approaches, with competitive localization performance.