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arXiv · 2508.21458

Federated Fine-tuning of SAM-Med3D for MRI-based Dementia Classification

Kaouther Mouheb·Marawan Elbatel·Janne Papma·Geert Jan Biessels·Jurgen Claassen·Huub Middelkoop·Barbara van Munster·Wiesje van der Flier·Inez Ramakers·Stefan Klein·Esther E. Bron
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Abstract

While foundation models (FMs) offer strong potential for AI-based dementia diagnosis, their integration into federated learning (FL) systems remains underexplored. In this benchmarking study, we systematically evaluate the impact of key design choices: classification head architecture, fine-tuning strategy, and aggregation method, on the performance and efficiency of federated FM tuning using brain MRI data. Using a large multi-cohort dataset, we find that the architecture of the classification head substantially influences performance, freezing the FM encoder achieves comparable results to full fine-tuning, and advanced aggregation methods outperform standard federated averaging. Our results offer practical insights for deploying FMs in decentralized clinical settings and highlight trade-offs that should guide future method development.

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Kaouther Mouheb, Marawan Elbatel, Janne Papma, Geert Jan Biessels, Jurgen Claassen, Huub Middelkoop, Barbara van Munster, Wiesje van der Flier, Inez Ramakers, Stefan Klein, Esther E. Bron. 2025-08-29. Federated Fine-tuning of SAM-Med3D for MRI-based Dementia Classification. https://doi.org/10.1007/978-3-032-05663-4_7

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