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Yuning Yang

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5 papers

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JBHI Journal 2025 Journal Article

FedPC: An Efficient Prototype-Based Clustered Federated Learning on Medical Imaging

Tianrun Gao
Keyan Liu
Yuning Yang
Xiaohong Liu
Ping Zhang
Guangyu Wang

Federated learning (FL) has emerged as a promising distributed paradigm that enables collaborative model training while preserving data privacy, but it suffers from performance degradation due to data heterogeneity. Although clustered federated learning (CFL) attempts to address this challenge by grouping clients with similar data distributions, existing methods are inefficient in capturing client data representations, leading to incorrect cluster identities and inferior cluster performance. To overcome these limitations, we propose an efficient prototype-based CFL framework (FedPC). Specifically, we introduce a dual-prototype strategy combining specific prototypes and generalized prototypes to capture class representations for cluster identities, along with a prototype-contrastive training mechanism that maximizes intra-cluster prototype consistency to improve cluster performance. Extensive experiments on medical imaging datasets (BloodMNIST and DermaMNIST) demonstrate that the FedPC outperforms nine state-of-the-art (SOTA) approaches, achieving average improvements of 2. 17% and 3. 47%, respectively. Furthermore, the FedPC reduces communication overhead by 3. 33 to 5. 68 times compared to SOTA methods, showcasing its efficiency in real-world FL scenarios.

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AAAI Conference 2025 Conference Paper

Flexible Sharpness-Aware Personalized Federated Learning

Xinda Xing
Qiugang Zhan
Xiurui Xie
Yuning Yang
Qiang Wang
Guisong Liu

Personalized federated learning (PFL) is a new paradigm to address the statistical heterogeneity problem in federated learning. Most existing PFL methods focus on leveraging global and local information such as model interpolation or parameter decoupling. However, these methods often overlook the generalization potential during local client learning. From a local optimization perspective, we propose a simple and general PFL method, Federated learning with Flexible Sharpness-Aware Minimization (FedFSA). Specifically, we emphasize the importance of applying a larger perturbation to critical layers of the local model when using the Sharpness-Aware Minimization (SAM) optimizer. Then, we design a metric, perturbation sensitivity, to estimate the layer-wise sharpness of each local model. Based on this metric, FedFSA can flexibly select the layers with the highest sharpness to employ larger perturbation. Extensive experiments are conducted on four datasets with two types of statistical heterogeneity for image classification. The results show that FedFSA outperforms seven state-of-the-art baselines by up to 8.26% in test accuracy. Besides, FedFSA can be applied to different model architectures and easily integrated into other federated learning methods, achieving a 4.45% improvement.

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TMLR Journal 2025 Journal Article

TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data

Namjoon Suh
Yuning Yang
Din-Yin Hsieh
Qitong Luan
Shirong Xu
Shixiang Zhu
Guang Cheng

We present \texttt{TimeAutoDiff}, a unified latent-diffusion framework that addresses four fundamental time-series tasks—unconditional generation, missing-data imputation, forecasting, and time-varying-metadata conditional generation—within a single model that natively handles heterogeneous features (continuous, binary, and categorical). We unify these tasks through a simple masked-modeling strategy: a binary mask specifies which time feature cells are observed and which must be generated. To make this work on mixed data types, we pair a lightweight variational autoencoder (i.e., VAE)—which maps continuous, categorical, and binary variables into a continuous latent sequence—with a diffusion model that learns dynamics in that latent space, avoiding separate likelihoods for each data type while still capturing temporal and cross-feature structure.Two design choices give \texttt{TimeAutoDiff} clear speed and scalability advantages. First, the diffusion process samples a single latent trajectory for the full time horizon rather than denoising one timestep at a time; this whole-sequence sampling drastically reduces reverse-diffusion calls and yields an order-of-magnitude throughput gain. Second, the VAE compresses along the feature axis, so very wide tables are modeled in a lower-dimensional latent space, further reducing computational load. Empirical evaluation demonstrates that \texttt{TimeAutoDiff} matches or surpasses strong baselines in synthetic sequence fidelity (discriminative, temporal-correlation, and predictive metrics) and consistently lowers MAE/MSE for imputation and forecasting tasks. Time-varying metadata conditioning unlocks real-world scenario exploration: by editing metadata sequences, practitioners can generate coherent families of counterfactual trajectories that track intended directional changes, preserve cross-feature dependencies, and remain conditionally calibrated—making "what-if" analysis practical. Our ablation studies confirm that performance is impacted by key architectural choices, such as the VAE's continuous feature encoding and specific components of the DDPM denoiser. Furthermore, a distance-to-closest-record (DCR) audit demonstrates that the model achieves generalization with limited memorization given enough dataset. Code implementations of \texttt{TimeAutoDiff} are provided in https://github.com/namjoonsuh/TimeAutoDiff.

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JBHI Journal 2024 Journal Article

Dense Contrastive-Based Federated Learning for Dense Prediction Tasks on Medical Images

Yuning Yang
Xiaohong Liu
Tianrun Gao
Xiaodong Xu
Ping Zhang
Guangyu Wang

Deep learning (DL) models have achieved remarkable success in various domains. But training an accurate DL model requires large amounts of data, which can be challenging to obtain in medical settings due to privacy concerns. Recently, federated learning (FL) has emerged as a promising solution that shares local models instead of raw data. However, FL in medical settings faces challenges of client drift due to the data heterogeneity across dispersed institutions. Although there exist studies to address this challenge, they mainly focus on the classification tasks that learn global representation of an entire image. Few have been studied on the dense prediction tasks, such as object detection. In this study, we propose dense contrastive-based federated learning (DCFL) tailored for dense prediction tasks in FL settings. DCFL introduces dense contrastive learning to FL, which aligns the local optimization objectives towards the global objective by maximizing the agreement of representations between the global and local models. Moreover, to improve the performance of dense target prediction at each level, DCFL applies multi-scale contrastive representation by utilizing multi-scale representations with dense features in contrastive learning. We evaluated DCFL on a set of realistic datasets for pulmonary nodule detection. DCFL demonstrates an overall performance improvement compared with the other federated learning methods in heterogeneous settings–improving the mean average precision by 4. 13% and testing recall by 6. 07% in highly heterogeneous settings.

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JMLR Journal 2015 Journal Article

Learning with the Maximum Correntropy Criterion Induced Losses for Regression

Yunlong Feng
Xiaolin Huang
Lei Shi
Yuning Yang
Johan A.K. Suykens

Within the statistical learning framework, this paper studies the regression model associated with the correntropy induced losses. The correntropy, as a similarity measure, has been frequently employed in signal processing and pattern recognition. Motivated by its empirical successes, this paper aims at presenting some theoretical understanding towards the maximum correntropy criterion in regression problems. Our focus in this paper is two-fold: first, we are concerned with the connections between the regression model associated with the correntropy induced loss and the least squares regression model. Second, we study its convergence property. A learning theory analysis which is centered around the above two aspects is conducted. From our analysis, we see that the scale parameter in the loss function balances the convergence rates of the regression model and its robustness. We then make some efforts to sketch a general view on robust loss functions when being applied into the learning for regression problems. Numerical experiments are also implemented to verify the effectiveness of the model. [abs] [ pdf ][ bib ] &copy JMLR 2015. ( edit, beta )

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