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

arXiv: https://arxiv.org/abs/2610.03474 · HTML: https://mlsanity.com/paper/2610.03474/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 2 Oct 2026.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients](https://mlsanity.com/paper/2010.01264/) (2020)
- [REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs](https://mlsanity.com/paper/1910.03667/) (2019)
- [FedKD: Communication Efficient Federated Learning via Knowledge Distillation](https://mlsanity.com/paper/2108.13323/) (2021)
- [Federated Learning for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2306.05980/) (2023)
- [The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI](https://mlsanity.com/paper/2405.18368/) (2024)
- [Fair Federated Medical Image Segmentation via Client Contribution Estimation](https://mlsanity.com/paper/2303.16520/) (2023)
- [FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation](https://mlsanity.com/paper/2205.01840/) (2022)
- [Federated Learning with Heterogeneous Architectures using Graph HyperNetworks](https://mlsanity.com/paper/2201.08459/) (2022)
- [Recurrent Early Exits for Federated Learning with Heterogeneous Clients](https://mlsanity.com/paper/2405.14791/) (2024)
- [A Survey on Model-heterogeneous Federated Learning: Problems, Methods, and Prospects](https://mlsanity.com/paper/2312.12091/) (2023)
- [Federated Learning for Inference at Anytime and Anywhere](https://mlsanity.com/paper/2212.04084/) (2022)
- [Memory-adaptive Depth-wise Heterogeneous Federated Learning](https://mlsanity.com/paper/2303.04887/) (2023)
- [Federated Learning in IoT: a Survey from a Resource-Constrained Perspective](https://mlsanity.com/paper/2308.13157/) (2023)
- [A Review on Security and Privacy of Internet of Medical Things](https://mlsanity.com/paper/2009.05394/) (2020)

## Abstract (arXiv)

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.