FedCLAM: Client Adaptive Momentum with Foreground Intensity Matching for Federated Medical Image Segmentation

TL;DR

FedCLAM is a federated learning method for medical image segmentation that adaptively handles client heterogeneity through momentum and intensity matching, leading to improved performance across diverse datasets.

Contribution

The paper introduces FedCLAM, a novel federated learning approach with client-adaptive momentum and intensity alignment for better medical image segmentation across heterogeneous data sources.

Findings

FedCLAM outperforms eight state-of-the-art methods in medical segmentation tasks.

The intensity alignment loss effectively handles heterogeneous image intensities.

Client-adaptive momentum improves model convergence and robustness.

Abstract

Federated learning is a decentralized training approach that keeps data under stakeholder control while achieving superior performance over isolated training. While inter-institutional feature discrepancies pose a challenge in all federated settings, medical imaging is particularly affected due to diverse imaging devices and population variances, which can diminish the global model's effectiveness. Existing aggregation methods generally fail to adapt across varied circumstances. To address this, we propose FedCLAM, which integrates \textit{client-adaptive momentum} terms derived from each client's loss reduction during local training, as well as a \textit{personalized dampening factor} to curb overfitting. We further introduce a novel \textit{intensity alignment} loss that matches predicted and ground-truth foreground distributions to handle heterogeneous image intensity profiles across institutions and devices. Extensive evaluations on two datasets show that FedCLAM surpasses eight cutting-edge methods in medical segmentation tasks, underscoring its efficacy. The code is available at https://github.com/siomvas/FedCLAM.