Accurate and robust segmentation of heterogeneous brain tumors is critical for individualized treatment planning, yet the integration of diverse multicenter datasets remains challenging due to patient privacy constraints. The BraTS Challenge Generalizability Task (GoAT) has highlighted the importance of developing models that generalize across multiple tumor subtypes, including adult glioma, meningioma, and brain metastasis, along with pediatric and sub-Saharan cohorts. In this work, we present BraTS-FL, a federated learning (FL) approach integrated with the nnU-Net framework to collaboratively train segmentation models across distinct tumor subtypes without sharing raw data between institutions. We design a multi-client FL setup, with each client specializing in a specific tumor subtype and employing harmonized preprocessing and training via the MONet bundle. Comparative evaluation on the BraTS 2025 generalizability validation set demonstrates that BraTS-FL achieves competitive performance compared to centralized nnU-Net training in terms of Dice and surface-based metrics across all tumor subregions. These findings underscore FL’s viability for privacy-preserving, scalable, and generalizable brain tumor segmentation in real-world heterogeneous clinical settings.