Deep learning models for brain tumor analysis require large and diverse datasets that are often siloed across healthcare institutions due to privacy regulations. We present a federated learning framework for brain tumor localization that enables multi-institutional collaboration without sharing sensitive patient data. Our method extends a hybrid Transformer-Graph Neural Network architecture derived from prior decoder-free supervoxel GNNs and is deployed within CAFEIN\textsuperscript{\textregistered}, CERN's federated learning platform designed for healthcare environments. We provide an explainability analysis through Transformer attention mechanisms that reveals which MRI modalities drive the model predictions. Experiments on the BraTS dataset demonstrate a key finding: while isolated training on individual client data triggers early stopping well before reaching full training capacity, federated learning enables continued model improvement by leveraging distributed data, ultimately matching centralized performance. This result provides strong justification for federated learning when dealing with complex tasks and high-dimensional input data, as aggregating knowledge from multiple institutions significantly benefits the learning process. Our explainability analysis, validated through rigorous statistical testing on the full test set (paired t-tests with Bonferroni correction), reveals that deeper network layers significantly increase attention to T2 and FLAIR modalities ($p<0.001$, Cohen's $d$=1.50), aligning with clinical practice.

翻译：脑肿瘤分析的深度学习模型通常需要大规模多样化数据集，但由于隐私法规限制，这些数据往往分散存储于各医疗机构。本文提出一种用于脑肿瘤定位的联邦学习框架，支持多机构在不共享敏感患者数据的前提下开展协作。该方法扩展了混合Transformer-图神经网络架构（源自先前的无解码器超体素GNN），并部署于CAFEIN®平台——这是CERN专为医疗环境设计的联邦学习平台。我们通过Transformer注意力机制提供可解释性分析，揭示驱动模型预测的关键MRI模态。在BraTS数据集上的实验证明了一个关键发现：虽然基于单个客户端数据的孤立训练会过早触发早停机制而无法充分发挥训练潜力，但联邦学习通过利用分布式数据实现了模型的持续改进，最终达到与集中式训练相当的性能。这一结果为处理复杂任务和高维输入数据时采用联邦学习提供了有力依据，因为聚合多机构知识能显著促进学习过程。我们的可解释性分析（通过对完整测试集的严格统计检验验证，包括经Bonferroni校正的配对t检验）表明：更深层的网络会显著增强对T2和FLAIR模态的关注度（$p<0.001$，Cohen's $d$=1.50），这与临床实践高度吻合。