Towards Personalized Federated Learning via Heterogeneous Model Reassembly

This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures. To track this problem, we propose a novel framework called pFedHR, which leverages heterogeneous model reassembly to achieve personalized federated learning. In particular, we approach the problem of heterogeneous model personalization as a model-matching optimization task on the server side. Moreover, pFedHR automatically and dynamically generates informative and diverse personalized candidates with minimal human intervention. Furthermore, our proposed heterogeneous model reassembly technique mitigates the adverse impact introduced by using public data with different distributions from the client data to a certain extent. Experimental results demonstrate that pFedHR outperforms baselines on three datasets under both IID and Non-IID settings. Additionally, pFedHR effectively reduces the adverse impact of using different public data and dynamically generates diverse personalized models in an automated manner

MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation

Health risk prediction is one of the fundamental tasks under predictive modeling in the medical domain, which aims to forecast the potential health risks that patients may face in the future using their historical Electronic Health Records (EHR). Researchers have developed several risk prediction models to handle the unique challenges of EHR data, such as its sequential nature, high dimensionality, and inherent noise. These models have yielded impressive results. Nonetheless, a key issue undermining their effectiveness is data insufficiency. A variety of data generation and augmentation methods have been introduced to mitigate this issue by expanding the size of the training data set through the learning of underlying data distributions. However, the performance of these methods is often limited due to their task-unrelated design. To address these shortcomings, this paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. Furthermore, MedDiffusion discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data. Experimental evaluation on four real-world medical datasets demonstrates that MedDiffusion outperforms 14 cutting-edge baselines in terms of PR-AUC, F1, and Cohen's Kappa. We also conduct ablation studies and benchmark our model against GAN-based alternatives to further validate the rationality and adaptability of our model design. Additionally, we analyze generated data to offer fresh insights into the model's interpretability

Imaging thermally fluctuating N\`eel vectors in van der Waals antiferromagnet NiPS3

Studying antiferromagnetic domains is essential for fundamental physics and potential spintronics applications. Despite its importance, few systematic studies have been performed on van der Waals (vdW) antiferromagnets (AFMs) domains with high spatial resolutions, and direct probing of the N\`eel vectors remains challenging. In this work, we found a multidomain in vdW AFM NiPS3, a material extensively investigated for its exotic magnetic exciton. We employed photoemission electron microscopy combined with the X-ray magnetic linear dichroism (XMLD-PEEM) to image the NiPS3's magnetic structure. The nanometer-spatial resolution of XMLD-PEEM allows us to determine local N\`eel vector orientations and discover thermally fluctuating N\'eel vectors that are independent of the crystal symmetry even at 65 K, well below TN of 155 K. We demonstrate a Ni ions' small in-plane orbital moment anisotropy is responsible for the weak magneto-crystalline anisotropy. The observed multidomain's thermal fluctuations may explain the broadening of magnetic exciton peaks at higher temperatures

Growth, Physiological, Biochemical, and Ionic Responses of Morus alba L. Seedlings to Various Salinity Levels

Mulberry (Morus alba L.), a moderately salt-tolerant tree species, is considered to be economically important. In this study, 1-year-old mulberry seedlings cultivated in soil under greenhouse conditions were treated with five concentrations of sodium chloride (NaCl; 0%, 0.1%, 0.2%, 0.3%, and 0.5%) for 3 and 21 days. Plant growth parameters were not affected by 0.1% NaCl, but significant reductions were observed after treatment with 0.2%, 0.3%, and 0.5% NaCl. The malondialdehyde content and cell membrane stability of mulberry seedlings exposed to 0.1% NaCl did not change, indicating that mulberry is not significantly affected by low-salinity conditions. The Na contents of various organs did not increase significantly in response to 0.1% NaCl, but the K:Na, Mg:Na, and Ca:Na ratios of various organs were affected by NaCl. Marked changes in the levels of major compatible solutes (proline, soluble sugars, and soluble proteins) occurred in both the leaves and roots of NaCl-treated seedlings relative to control seedlings. Under severe saline conditions (0.5% NaCl), the ability of mulberry to synthesize enzymatic antioxidants may be impaired