We propose FedCLAM, a federated learning method that addresses data heterogeneity in medical image segmentation through client-adaptive momentum and foreground intensity matching. Our approach achieves state-of-the-art performance on cardiac MRI and abdominal CT segmentation tasks.
MICCAI 2025 / Spotlight (Top 11%)
Federated Learning (FL) enables collaborative training across institutions without sharing sensitive medical data. However, heterogeneous data distributions across clients remain a major challenge, particularly in medical image segmentation where intensity variations and class imbalances are common.
We introduce FedCLAM (Federated Client-Adaptive Momentum), a novel aggregation strategy that adapts momentum coefficients based on client-specific data characteristics. Our method incorporates foreground intensity matching to align feature distributions across clients, improving segmentation consistency without compromising privacy.
Our work makes the following contributions:
Client-Adaptive Momentum: A dynamic momentum aggregation scheme that weights client contributions based on their data characteristics and training stability.
Foreground Intensity Matching: A privacy-preserving technique that aligns intensity distributions across clients to reduce domain shift effects.
State-of-the-Art Results: +3.8% Dice improvement on cardiac MRI segmentation and +2.1% on abdominal CT segmentation compared to previous methods.
We evaluate FedCLAM on two challenging medical image segmentation benchmarks: cardiac MRI (M&Ms dataset) and abdominal CT (BTCV dataset). Our method consistently outperforms existing federated learning approaches including FedAvg, FedProx, and SCAFFOLD.
On cardiac MRI segmentation with 4 heterogeneous clients, FedCLAM achieves 87.2% Dice score compared to 83.4% for FedAvg, representing a significant improvement in cross-institutional performance.
If you find this work useful, please cite our paper:
@inproceedings{siomos2025fedclam,
title={FedCLAM: Client Adaptive Momentum with Foreground Intensity
Matching for Federated Medical Image Segmentation},
author={Siomos, Vasilis and Passerat-Palmbach, Jonathan and Tarroni, Giacomo},
booktitle={International Conference on Medical Image Computing and
Computer-Assisted Intervention (MICCAI)},
year={2025}
}