Federated and GAN-Augmented Deep Learning Frameworks for Brain Tumor Diagnosis: A Comprehensive Survey

Deep learning has transformed brain tumor detection and segmentation, yet clinical translation is impeded by data scarcity, class imbalance, heterogeneous imaging protocols, interpretability gaps and heavy computational demands This review brings together recent developments and highlights Generative Adversarial Networks (GANs) and Federated Learning as central elements in practical applications. GANs expand limited datasets with high‑fidelity synthetic images that improve diversity, robustness and resolution for training, while federated learning enables privacy‑preserving, multi‑institutional model development that enhances generalization without exchanging raw patient data. We contrast GANs with VAEs and diffusion models, highlighting trade-offs among fidelity, interpretability, stability and efficiency, and examine hybrid CNN-transformer architectures, multimodal fusion, radiomics and optimization strategies that bolster performance. Key future directions include hybrid generative frameworks, explainable AI for transparent grading, fine‑grained tumor staging, multimodal integration, scalable architectures for resource‑limited settings and standardized benchmarks for reproducibility. By situating GANs and federated learning within an ethical and reproducible framework, AI systems may be developed to support clinical reliability, safeguard privacy and prioritize patient needs by helping to connect research with practical application.