When inducing Decision Trees, Windowing consists in selecting a random subset of the available training instances (the window) to induce a tree, and then enhance it by adding counter examples, i.e., instances not covered by the tree, to the window for inducing a new tree. The process iterates until all instances are well classified or no accuracy is gained. In favorable domains, the technique is known to speed up the induction process, and to enhance the accuracy of the induced tree; while reducing the number of training instances used. In this paper, a Windowing based strategy exploiting an optimized search of counter examples through the use of GPUs is introduced to cope with Distributed Data Mining (DDM) scenarios. The strategy is defined and implemented in JaCa-DDM, a novel system founded on the Agents & Artifacts paradigm. Our approach is well suited for DDM problems generating large amounts of training instances. Some experiments in diverse domains compare our strategy with the traditional centralized approach, including an exploratory case study on pixel-based segmentation for the detection of precancerous cervical lesions on colposcopic images.