Federated Semi-Supervised Image Segmentation with Dynamic Client Selection

LIU Zhenbing; LI Huanlan; WANG Baoyuan; LU Haoxiang; PAN Xipeng

doi:10.11999/JEIT250834

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LIU Zhenbing, LI Huanlan, WANG Baoyuan, LU Haoxiang, PAN Xipeng. Federated Semi-Supervised Image Segmentation with Dynamic Client Selection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250834

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LIU Zhenbing, LI Huanlan, WANG Baoyuan, LU Haoxiang, PAN Xipeng. Federated Semi-Supervised Image Segmentation with Dynamic Client Selection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250834

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Federated Semi-Supervised Image Segmentation with Dynamic Client Selection

doi: 10.11999/JEIT250834 cstr: 32379.14.JEIT250834

1.
Guilin University of Electronic Technology, School of Computer Science and Information Security, Guilin 541004, China
2.
Department of Radiology, Guangdong Provincial People’s Hospital, Southern Medical University, Guangzhou 510080, China
3.
Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China

Funds: The National Natural Science Foundation of China (82502451, 82272075, 62462017), The Natural Science Foundation of Guangxi (2025GXNSFBA069390), The Key Research and Development Program of Guangxi (AB2401008)

Accepted Date: 2025-12-29
Rev Recd Date: 2025-12-29

Available Online: 2026-01-08

Abstract

Abstract

Objective Multicenter validation is an inevitable trend in clinical research, yet strict privacy regulations, heterogeneous cross-institutional data distributions and scarce pixel-level annotations limit the applicability of conventional centralized medical image segmentation models. This study aims to develop a federated semi-supervised framework that jointly exploits labelled and unlabeled prostate MRI data from multiple hospitals, explicitly considering dynamic client participation and non-independent and identically distributed (Non-IID) data, so as to improve segmentation accuracy and robustness under real-world constraints. Methods A cross-silo federated semi-supervised learning paradigm is adopted, in which clients with pixel-wise annotations act as labeled clients and those without annotations act as unlabeled clients. Each client maintains a local student network for prostate segmentation. On unlabeled clients, a teacher network with the same architecture is updated by the exponential moving average of student parameters and generates perturbed pseudo-labels to supervise the student through a hybrid consistency loss that combines Dice and binary cross-entropy terms. To mitigate the negative influence of heterogeneous and low-quality updates, a performance-driven dynamic client selection and aggregation strategy is introduced. At each communication round, clients are evaluated on their local validation sets, and only those whose Dice scores exceed a threshold are retained; then a Top-K subset is aggregated with normalized contribution weights derived from validation Dice, with bounds to avoid gradient vanishing and single-client dominance. For unlabeled clients, a penalty factor is applied to down-weight unreliable pseudo-labeled updates. As the segmentation backbone, a Multi-scale Feature Fusion U-Net (MFF-UNet) is constructed. Starting from a standard encoder–decoder U-Net, an FPN-like pyramid is inserted into the encoder, where multi-level feature maps are channel-aligned by 1×1 convolutions, fused in a top–down pathway by upsampling and element-wise addition, and refined using 3×3 convolutions. The decoder progressively upsamples these fused features and combines them with encoder features via skip connections, enabling joint modelling of global semantics and fine-grained boundaries. The framework is evaluated on T2-weighted prostate MRI from six centers: three labeled clients and three unlabeled clients. All 3D volumes are resampled, sliced into 2D axial images, resized and augmented. Dice coefficient and 95th percentile Hausdorff distance (HD95) are used as evaluation metrics. Results and Discussions On the six-center dataset, the proposed method achieves average Dice scores of 0.8405 on labeled clients and 0.7868 on unlabeled clients, with corresponding HD95 values of 8.04 and 8.67 pixels, respectively. These results are consistently superior to or on par with several representative federated semi-supervised or mixed-supervision methods, and the improvements are most pronounced on distribution-shifted unlabeled centers. Qualitative visualization shows that the proposed method produces more complete and smoother prostate contours with fewer false positives in challenging low-contrast or small-volume cases, compared with the baselines. Attention heatmaps extracted from the final decoder layer demonstrate that U-Net suffers from attention drift, SegMamba displays diffuse responses and nnU-Net exhibits weak activations for small lesions, whereas MFF-UNet focuses more precisely on the prostate region with stable high responses, indicating enhanced discriminative capability and interpretability. Conclusions A federated semi-supervised prostate MRI segmentation framework that integrates teacher–student consistency learning, multi-scale feature fusion and performance-driven dynamic client selection is presented. The method preserves patient privacy by keeping data local, alleviates annotation scarcity by exploiting unlabeled clients and explicitly addresses client heterogeneity through reliability-aware aggregation. Experiments on a six-center dataset demonstrate that the proposed framework achieves competitive or superior overlap and boundary accuracy compared with state-of-the-art federated semi-supervised methods, particularly on distribution-shifted unlabeled centers. The framework is model-agnostic and can be extended to other organs, imaging modalities and cross-institutional segmentation tasks under stringent privacy and regulatory constraints.
- Medical Image Segmentation,
- Federated Semi-Supervised Learning (FSSL),
- Privacy-Preserving,
- Dynamic Aggregation

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References(37)

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