Adversarial Versus Federated: An Adversarial Learning based Multi-Modality Cross-Domain Federated Medical Segmentation

• URL: http://arxiv.org/abs/2509.23907v1
• Date: Sun, 28 Sep 2025 14:26:04 GMT
• Title: Adversarial Versus Federated: An Adversarial Learning based Multi-Modality Cross-Domain Federated Medical Segmentation
• Authors: You Zhou, Lijiang Chen, Shuchang Lyu, Guangxia Cui, Wenpei Bai, Zheng Zhou, Meng Li, Guangliang Cheng, Huiyu Zhou, Qi Zhao,
• Abstract summary: Federated learning enables collaborative training of machine learning models among different clients.<n>We propose a new Federated Domain Adaptation (FedDA) segmentation training framework.<n>Our proposed FedDA substantially achieves cross-domain federated aggregation, endowing single modality client with cross-modality processing capabilities.
• Score: 30.99222543580891
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning enables collaborative training of machine learning models among different clients while ensuring data privacy, emerging as the mainstream for breaking data silos in the healthcare domain. However, the imbalance of medical resources, data corruption or improper data preservation may lead to a situation where different clients possess medical images of different modality. This heterogeneity poses a significant challenge for cross-domain medical image segmentation within the federated learning framework. To address this challenge, we propose a new Federated Domain Adaptation (FedDA) segmentation training framework. Specifically, we propose a feature-level adversarial learning among clients by aligning feature maps across clients through embedding an adversarial training mechanism. This design can enhance the model's generalization on multiple domains and alleviate the negative impact from domain-shift. Comprehensive experiments on three medical image datasets demonstrate that our proposed FedDA substantially achieves cross-domain federated aggregation, endowing single modality client with cross-modality processing capabilities, and consistently delivers robust performance compared to state-of-the-art federated aggregation algorithms in objective and subjective assessment. Our code are available at https://github.com/GGbond-study/FedDA.

Related papers

• Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization [0.0]
  In this work, we consider a realistic FL scenario where each client holds single-modality data (CT or MRI)<n>We evaluate convolution-based spatial augmentation, frequency-domain manipulation, domain-specific normalization, and global intensity nonlinear (GIN) augmentation.<n>Our federated approach achieves 93-98% of centralized training accuracy, demonstrating strong cross-modality generalization without compromising data privacy.
  arXiv  Detail & Related papers  (2026-02-24T11:13:01Z)
• pFedSAM: Personalized Federated Learning of Segment Anything Model for Medical Image Segmentation [23.86147005761286]
  We present the first personalized federated SAM framework tailored for heterogeneous data scenarios in medical image segmentation.<n>Our framework integrates two key innovations: (1) a personalized strategy that aggregates only the global parameters to capture cross-client commonalities while retaining the designed L-MoE component to preserve domain-specific features; and (2) a decoupled global-local fine-tuning mechanism that leverages a teacher-student paradigm via knowledge distillation to bridge the gap between the global shared model and the personalized local models, thereby mitigating overgeneralization.
  arXiv  Detail & Related papers  (2025-09-19T06:05:35Z)
• Federated Client-tailored Adapter for Medical Image Segmentation [21.964553228831427]
  We propose a novel Federated Client-tailored Adapter (FCA) framework for medical image segmentation.<n>FCA achieves stable and client-tailored adaptive segmentation without sharing sensitive local data.<n>We develop two client-tailored federated updating strategies that adaptively decompose the adapter into common and individual components.
  arXiv  Detail & Related papers  (2025-04-25T02:20:25Z)
• UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification Tasks [5.563171090433323]
  We introduce UniFed, a universal federated learning paradigm that aims to classify any disease from any imaging modality. Specifically, by dynamically adjusting both local and global models, UniFed considers the varying task complexities of clients and the server. We demonstrate the superiority of our framework in terms of accuracy, communication cost, and convergence time over relevant benchmarks in diagnosing retina, histopathology, and liver tumour diseases.
  arXiv  Detail & Related papers  (2024-07-29T23:15:15Z)
• Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training [75.40980802817349]
  Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. We introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites.
  arXiv  Detail & Related papers  (2023-08-31T00:36:10Z)
• A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation [18.402074964118697]
  This paper presents a solution to the cross-domain adaptation problem for 2D surgical image segmentation. Deep learning architectures in medical image analysis necessitate extensive training data for better generalization. We propose a Client-server deep federated architecture for cross-domain adaptation.
  arXiv  Detail & Related papers  (2023-06-14T19:49:47Z)
• Combating Exacerbated Heterogeneity for Robust Models in Federated Learning [91.88122934924435]
  Combination of adversarial training and federated learning can lead to the undesired robustness deterioration. We propose a novel framework called Slack Federated Adversarial Training (SFAT) We verify the rationality and effectiveness of SFAT on various benchmarked and real-world datasets.
  arXiv  Detail & Related papers  (2023-03-01T06:16:15Z)
• FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Data [69.0785021613868]
  Federated learning is a distributed machine learning approach which enables a shared server model to learn by aggregating the locally-computed parameter updates with the training data from spatially-distributed client silos. We propose the Federated Invariant Learning Consistency (FedILC) approach, which leverages the gradient covariance and the geometric mean of Hessians to capture both inter-silo and intra-silo consistencies. This is relevant to various fields such as medical healthcare, computer vision, and the Internet of Things (IoT)
  arXiv  Detail & Related papers  (2022-05-19T03:32:03Z)
• Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation [66.44449514373746]
  Cross-silo federated learning (FL) has attracted much attention in medical imaging analysis with deep learning in recent years. There can be a gap between the model trained from FL and one from centralized training. We propose a novel training framework FedSM to avoid client issue and successfully close the drift gap.
  arXiv  Detail & Related papers  (2022-03-18T19:50:07Z)
• FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space [63.43592895652803]
  Federated learning allows distributed medical institutions to collaboratively learn a shared prediction model with privacy protection. While at clinical deployment, the models trained in federated learning can still suffer from performance drop when applied to completely unseen hospitals outside the federation. We present a novel approach, named as Episodic Learning in Continuous Frequency Space (ELCFS), for this problem. The effectiveness of our method is demonstrated with superior performance over state-of-the-arts and in-depth ablation experiments on two medical image segmentation tasks.
  arXiv  Detail & Related papers  (2021-03-10T13:05:23Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.