Author name cluster

Guibo Luo

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

DSFedMed: Dual-Scale Federated Medical Image Segmentation via Mutual Distillation Between Foundation and Lightweight Models

Hanwen Zhang
Qiaojin Shen
Yuxi Liu
Yuesheng Zhu
Guibo Luo

Foundation Models (FMs) have demonstrated strong generalization across diverse vision tasks. However, their deployment in federated settings is hindered by high computational demands, substantial communication overhead, and significant inference costs. We propose DSFedMed, a dual-scale federated framework that enables mutual knowledge distillation between a centralized foundation model and lightweight client models for medical image segmentation. To support knowledge distillation, a set of high-quality medical images is generated to replace real public datasets, and a learnability-guided sample selection strategy is proposed to enhance efficiency and effectiveness in dual-scale distillation. This mutual distillation enables the foundation model to transfer general knowledge to lightweight clients, while also incorporating client-specific insights to refine the foundation model. Evaluations on five medical imaging segmentation datasets show that DSFedMed achieves an average 2 percent improvement in Dice score while reducing communication costs and inference time by nearly 90 percent compared to existing federated foundation model baselines. These results demonstrate significant efficiency gains and scalability for resource-limited federated deployments.

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AAAI Conference 2026 Conference Paper

Feature-Aware One-Shot Federated Learning via Hierarchical Token Sequences

Shudong Liu
Hanwen Zhang
Xiuling Wang
Yuesheng Zhu
Guibo Luo

One-shot federated learning (OSFL) reduces the communication cost and privacy risks of iterative federated learning by constructing a global model with a single round of communication. However, most existing methods struggle to achieve robust performance on real-world domains such as medical imaging, or are inefficient when handling non-IID (Independent and Identically Distributed) data. To address these limitations, we introduce FALCON, a novel framework that enhances the effectiveness of OSFL over non-IID image data. The core idea of FALCON is to leverage the feature-aware hierarchical token sequences generation and knowledge distillation into OSFL. First, each client leverages a pretrained visual encoder with hierarchical scale encoding to compress images into hierarchical token sequences, which capture multi-scale semantics. Second, a multi-scale autoregressive transformer generator is used to model the distribution of these token sequences and generate the synthetic sequences. Third, clients upload the synthetic sequences along with the local classifier trained on the real token sequences to the server. Finally, the server incorporates knowledge distillation into global training to reduce reliance on precise distribution modeling. Experiments on medical and natural image datasets validate the effectiveness of FALCON in diverse non-IID scenarios, outperforming the best OSFL baselines by 9.58\% in average accuracy.

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ECAI Conference 2025 Conference Paper

A New One-Shot Federated Learning Framework for Medical Imaging Classification with Feature-Guided Rectified Flow and Knowledge Distillation

Yufei Ma
Hanwen Zhang 0007
Qiya Yang
Guibo Luo
Yuesheng Zhu

In multi-center scenarios, One-Shot Federated Learning (OSFL) has attracted increasing attention due to its low communication overhead, requiring only a single round of transmission. However, existing generative model-based OSFL methods suffer from low training efficiency and potential privacy leakage in the healthcare domain. Additionally, achieving convergence within a single round of model aggregation is challenging under non-Independent and Identically Distributed (non-IID) data. To address these challenges, in this paper a modified OSFL framework is proposed, in which a new Feature-Guided Rectified Flow Model (FG-RF) and Dual-Layer Knowledge Distillation (DLKD) aggregation method are developed. FG-RF on the client side accelerates generative modeling in medical imaging scenarios while preserving privacy by synthesizing feature-level images rather than pixel-level images. To handle non-IID distributions, DLKD enables the global student model to simultaneously mimic the output logits and align the intermediate-layer features of client-side teacher models during aggregation. Experimental results on three non-IID medical imaging datasets show that our new framework and method outperform multi-round federated learning approaches, achieving up to 21. 73% improvement, and exceed the baseline FedISCA by an average of 21. 75%. Furthermore, our experiments demonstrate that feature-level synthetic images significantly reduce privacy leakage risks compared to pixel-level synthetic images. The code is available at https: //github. com/LMIAPC/one-shot-fl-medical.

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JBHI Journal 2025 Journal Article

Federated Learning for Medical Image Classification: A Comprehensive Benchmark

Zhekai Zhou
Guibo Luo
Mingzhi Chen
Zhenyu Weng
Yuesheng Zhu

The federated learning (FL) paradigm is well-suited for the field of medical image analysis, as it can effectively cope with machine learning on isolated multi-center data while protecting the privacy of participating parties. However, current research on optimization algorithms in FL often focuses on limited datasets and scenarios, primarily centered around natural images, with insufficient comparative experiments in medical contexts. In this work, we conduct a comprehensive evaluation of several state-of-the-art FL algorithms in the context of medical imaging. We conduct a fair comparison of classification models trained using various FL algorithms across multiple medical imaging datasets. Additionally, we evaluate system performance metrics, such as communication cost and computational efficiency, while considering different FL architectures. Our findings show that medical imaging datasets pose substantial challenges for current FL optimization algorithms. No single algorithm consistently delivers optimal performance across all medical FL scenarios, and many optimization algorithms may under-perform when applied to these datasets. Our experiments provide a benchmark and guidance for future research and application of FL in medical imaging contexts. Furthermore, we propose an efficient and robust method that combines generative techniques using denoising diffusion probabilistic models with label smoothing to augment datasets, widely enhancing the performance of FL on classification tasks across various medical imaging datasets.

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JBHI Journal 2025 Journal Article

Non-IID Medical Image Segmentation Based on Cascaded Diffusion Model for Diverse Multi-Center Scenarios

Hanwen Zhang
Mingzhi Chen
Yuxi Liu
Guibo Luo
Yuesheng Zhu

Learning from multi-center medical datasets to obtain a high-performance global model is challenging due to the privacy protection and data heterogeneity in healthcare systems. Current federated learning approaches are not efficient enough to learn Non-Independent and Identically Distributed (Non-IID) data and require high communication costs. In this work, a practical privacy computing framework is proposed to train a Non-IID medical image segmentation model under various multi-center setting in low communication cost. Specifically, an efficient cascaded diffusion model is trained to generate image-mask pairs that have similar distribution to the training data of clients, providing rich labeled data on client side to mitigate heterogeneity. Also, a label construction module is developed to improve the quality of generated image-mask pairs. Moreover, a set of aggregation methods is proposed to achieve global model from data generated from Cascaded Diffusion model for diverse scenarios: CD-Syn, CD-Ens and its extension CD-KD. CD-Syn is a one-shot method that trains segmentation model solely on public generated datasets while CD-Ens and CD-KD maximize the utilization of local original data by an extra communication round of ensemble or knowledge distillation. In this way, the setting of our proposed framework is highly practical, providing multiple aggregation methods which can flexibly adapt to varying demands for efficiency, privacy, and accuracy. We systematically evaluated the effectiveness of our proposed framework on five Non-IID medical datasets and observe 5. 38% improvement in Dice score compared with baseline method (FednnU-Net) on average.

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AAAI Conference 2025 Conference Paper

Robust Image Hashing Based on Contrastive Masked Autoencoder with Weak-Strong Augmentation Alignment

Cundian Yang
Guibo Luo
Yuesheng Zhu
Jiaqi Li
Xiyao Liu

Recently, numerous robust image hashing schemes have been developed for content identification. However, many of these schemes face the challenges of maintaining discrimination while simultaneously resisting large-scale attacks. In this paper, we propose a robust image hashing scheme based on Contrastive Masked Autoencoder with weak-strong augmentation Alignment (CMAA). Leveraging contrastive learning, CMAA is designed to learn features that are robust to large-scale and hybrid attacks while maintaining the discrimination of those features. Specifically, it utilizes distribution divergence to align weak attack augmented features with strong attack augmented features, namely weak-strong augmentation alignment, to enhance the robustness to strong attacks. In addition, a masked vision transformer is incorporated to further enhance content identification performance. CMAA also includes a parameter-free quantization layer to mitigate the loss induced by binarization. Experimental results demonstrate that our method exhibits remarkable robustness against various attacks, including challenging ones such as rotation and hybrid attacks, and delivers excellent identification performance with a F1 score close to 1.0. Our code and supplementary materials are available on Github.

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ECAI Conference 2024 Conference Paper

Cross-Stage Transfer in Multi-Stage Cascade Ranking and Filtering Systems

Yifan Pan
Guibo Luo
Yuesheng Zhu

Unsupervised Domain Adaptation (UDA) aims to transfer a model from a labeled source domain to an unlabeled target domain, addressing challenges of distinct data distributions, termed domain shift. Existing UDA research primarily focuses on classification-like tasks, but neglects ranking and filtering tasks essential for applications like medical diagnosis and search engines. This paper is the first to notice and identify a new real-world transfer problem: cross-stage transfer in multi-stage cascade ranking and filtering systems, a common issue in diverse applications, including information retrieval systems, medical diagnosis, and other real-world ranking/filtering systems. In this problem, we emphasize the crucial assumption of order-invariance and address the key issue named Cross-stage Class Concept Conflict (C4), highlighting potential inconsistencies in class concepts for the same sample at different stages. To tackle these challenges, we propose a novel method, Unsupervised Rank Adaptation (URA), comprising two key components: order-conditional distribution alignment, characterizing the order-conditional distribution intra-stage and aligning them across stages; and principal projection alignment, aligning the principal component’s projection matrix with classifier parameters to ensure order-invariance without guessing pseudo-labels, mitigating the influence of C4. Experimental results show that our approach reaches state-of-the-art performance in various cross-stage transfer tasks.

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IJCAI Conference 2023 Conference Paper

Robust Steganography without Embedding Based on Secure Container Synthesis and Iterative Message Recovery

Ziping Ma
Yuesheng Zhu
Guibo Luo
Xiyao Liu
Gerald Schaefer
Hui Fang

Synthesis-based steganography without embedding (SWE) methods transform secret messages to container images synthesised by generative networks, which eliminates distortions of container images and thus can fundamentally resist typical steganalysis tools. However, existing methods suffer from weak message recovery robustness, synthesis fidelity, and the risk of message leakage. To address these problems, we propose a novel robust steganography without embedding method in this paper. In particular, we design a secure weight modulation-based generator by introducing secure factors to hide secret messages in synthesised container images. In this manner, the synthesised results are modulated by secure factors and thus the secret messages are inaccessible when using fake factors, thus reducing the risk of message leakage. Furthermore, we design a difference predictor via the reconstruction of tampered container images together with an adversarial training strategy to iteratively update the estimation of hidden messages. This ensures robustness of recovering hidden messages, while degradation of synthesis fidelity is reduced since the generator is not included in the adversarial training. Extensive experimental results convincingly demonstrate that our proposed method is effective in avoiding message leakage and superior to other existing methods in terms of recovery robustness and synthesis fidelity.

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