In the backdrop of smart factory deployment, while similar rotating machinery managed by different enterprises holds potential for collaborative diagnosis, data privacy regulations prevent sharing. Additionally, operating condition differences result in non-independent and identically distributed data, limiting the generalization ability of diagnosis models across varying conditions. To tackle these challenges, this article proposes a personalized federated domain generalization framework. Without sharing local data, it enhances the generalization and robustness of edge-end diagnosis models through alternating adversarial optimization of inter-device collaboration and local personalized updates. The diagnosis model, built on the latent convolutional network, leverages input-driven feature adaptation for dynamic representation. During collaboration, publicly available datasets facilitate knowledge transfer in a shared space, while consistency constraints improve communication efficiency. In the local update phase, performance constraints and self-distillation preserve local knowledge, ensuring stable classification. Experiments on the Huazhong University of Science and Technology bearing dataset and the Mechanical Comprehensive Diagnosis Platform bearing dataset show that the proposed method achieves average accuracies of 88.96% and 92.33% under global operating conditions, respectively, outperforming several advanced approaches. Edge-end models optimized by the proposed approach improve cross-domain generalization while maintaining reliable local diagnosis performance.