A Survey on Federated Multi-Modal Hybrid CNN-Transformer with Optimized Random Graph Dilated Diffusion Attention for Brain Tumor Segmentation and Early Classification in MRI

Accurate and privacy-preserving segmentation and early categorization of brain cancers from multi-modal magnetic resonance imaging (MRI) is an important topic in computational neuro-oncology. CNNs excel in local feature extraction but not long-range context, while Transformers capture long-range dependencies but at much higher computational cost. Thus, hybrid CNN-Transformer designs have become the de facto standard for lesion delineation. Graph neural networks (GNNs) and diffusion-based generative refinement can model irregular tumor topology and denoise coarse predictions into sharper masks, while dilated and multi-scale attention mechanisms increase the effective receptive field without increasing cost. Privacy regulations have led to clinical MRI data being compartmentalized in hospitals, and federated learning (FL) has become the preferred training paradigm to construct generalizable models without centralizing raw scans. In this review, we survey recent literature (2023–2026) for five constituent technologies: hybrid CNN–Transformer segmentation, graph-attention modeling, diffusion-based refinement, dilated multi-scale attention and federated multi-modal learning. We discuss how their integration could plausibly result in a federated, multi-modal hybrid CNN–Transformer architecture with an optimized random-graph dilated diffusion attention module. We summarize representative approaches, datasets, assessment protocols, open difficulties (communication overhead, non-IID data, interpretability, computational cost) and point out possible future paths.