Abstract
Federated learning (FL) enables collaborative training of medical image segmentation models without sharing raw patient data, yet existing approaches assume a homogeneous compute budget across institutions, limiting participation of low-resource sites. We propose Fed-ADApt, a depth-adaptive federated framework for UNet-based segmentation that jointly addresses low-compute training and inference. Fed-ADApt integrates multi-depth supervision with hierarchical depth-wise aggregation, allowing each site to train according to its local compute budget while contributing to a global model that supports dynamic depth selection at deployment. We evaluated Fed-ADApt on multi-site 2D retinal fundus disc segmentation and 3D brain tumor segmentation. Across both tasks, federated collaboration substantially improves robustness under domain shift. Fed-ADApt matched the full-resource FedAvg performance in 3D and achieved competitive 2D performance with a 4.7% average Dice reduction, while reducing average inference cost by 19.5% in 3D and 34.5% in 2D and substantially reducing training cost by 98% at the most constrained sites. Importantly, Fed-ADApt enables low-resource institutions that cannot train full-capacity models to participate in federations while maintaining competitive global performance under a favorable accuracy to efficiency trade-off. By considering training and inference compute budgets, Fed-ADApt provides a practical and equitable solution for federated medical image segmentation across heterogeneous clinical and edge-enabled imaging environments.
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Cite this article
APA 7
Parida, A., Jiang, Z., Roshanitabrizi, P., Tapp, A., Ledesma-Carbayo, M. J., Anwar, S. M., Xu, Z., Linguraru, M. G., & Roth, H. R. (2026). Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation. https://omanscience.com/en/articles/fed-adapt-federated-anytime-depth-adaptation-for-resource-aware-medical-image-segmentation
MLA 9
Parida, Abhijeet, et al. "Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation." https://omanscience.com/en/articles/fed-adapt-federated-anytime-depth-adaptation-for-resource-aware-medical-image-segmentation.
Chicago (author–date)
Parida, Abhijeet, Zhifan Jiang, Pooneh Roshanitabrizi, Austin Tapp, Maria J. Ledesma-Carbayo, Syed Muhammad Anwar, Ziyue Xu, Marius George Linguraru, and Holger R. Roth. 2026. "Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation." https://omanscience.com/en/articles/fed-adapt-federated-anytime-depth-adaptation-for-resource-aware-medical-image-segmentation.
Harvard
Parida, A., Jiang, Z., Roshanitabrizi, P., Tapp, A., Ledesma-Carbayo, M. J., Anwar, S. M., Xu, Z., Linguraru, M. G. and Roth, H. R. (2026) 'Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation', Available at: https://omanscience.com/en/articles/fed-adapt-federated-anytime-depth-adaptation-for-resource-aware-medical-image-segmentation.
Vancouver
Parida A, Jiang Z, Roshanitabrizi P, Tapp A, Ledesma-Carbayo MJ, Anwar SM, et al. Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation. https://omanscience.com/en/articles/fed-adapt-federated-anytime-depth-adaptation-for-resource-aware-medical-image-segmentation
IEEE
A. Parida, Z. Jiang, P. Roshanitabrizi, A. Tapp, M. J. Ledesma-Carbayo, S. M. Anwar, Z. Xu, M. G. Linguraru, and H. R. Roth, "Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation," https://omanscience.com/en/articles/fed-adapt-federated-anytime-depth-adaptation-for-resource-aware-medical-image-segmentation.