Federated learning (FL) enables collaborative model training across distributed clients without requiring centralized access to local data. However, it faces significant challenges under domain shift, where discrepancies between client data distributions and deployment environments might degrade model performance. In this paper, we introduce a novel feedback-driven framework for enhancing domain adaptation in federated settings. Our approach incorporates server-side domain analysis to detect distributional shifts during validation and generates lightweight feedback signals such as Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) to highlight domain-specific patterns. These signals are transmitted to clients as adaptation cues, enabling more targeted local training. In addition, the framework supports client-side data generation using modality-appropriate generative models, including variational autoencoders for tabular data, diffusion models for images, and language models for text. This data generation further mitigates heterogeneity and strengthens model generalization. By combining server-guided feedback with client-side adaptation and data augmentation, our method significantly improves performance under domain shift. Extensive experiments demonstrate consistent gains over standard FL baselines across diverse tasks and data modalities.