Developing a unified adversarial architecture for privacy mechanism training in multi-group settings presents several challenges and considerations. One key challenge is the potential instability of the adversarial training process when multiple groups with varying private and utility features are involved. To address this, it is essential to carefully design the architecture to handle the complexities of multi-group scenarios, such as incorporating separate discriminators for each group's private and utility features. Additionally, ensuring convergence and avoiding mode collapse in the training process requires fine-tuning the hyperparameters and optimizing the loss functions for each group. Balancing the tradeoff between privacy and utility across multiple groups also requires a nuanced approach, as the objectives and constraints may differ for each group. By carefully addressing these challenges and considerations, a unified adversarial architecture can effectively train privacy mechanisms in multi-group settings.