Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation

2026-10-02

AI Summary

This study addresses the challenge of computational heterogeneity in federated medical image segmentation, which often restricts the participation of resource-constrained institutions. To this end, we propose a deep adaptive federated learning framework built upon a UNet architecture that incorporates multi-depth supervision and hierarchical aggregation mechanisms. This design enables participating nodes to dynamically select training and inference depths according to their local computational budgets, thereby achieving joint optimization of resource allocation. Experimental results demonstrate that the proposed framework attains performance comparable to full-capacity models on 3D segmentation tasks while substantially reducing both training and inference overhead. By facilitating equitable and efficient collaboration across heterogeneous environments, this approach effectively empowers low-resource institutions to participate in federated medical analysis.

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.

Problem

• Federated Learning
• Medical Image Segmentation
• Resource Heterogeneity
• Compute Budget
• Depth Adaptation

Innovation

• Federated Learning
• Depth Adaptation
• Medical Image Segmentation
• Resource-Aware
• Multi-depth Supervision