Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model

Recently, semi-supervised federated learning (semi-FL) has been proposed to handle the commonly seen real-world scenarios with labeled data on the server and unlabeled data on the clients. However, existing methods face several challenges such as communication costs, data heterogeneity, and training pressure on client devices. To address these challenges, we introduce the powerful diffusion models (DM) into semi-FL and propose FedDISC, a Federated Diffusion-Inspired Semi-supervised Co-training method. Specifically, we first extract prototypes of the labeled server data and use these prototypes to predict pseudo-labels of the client data. For each category, we compute the cluster centroids and domain-specific representations to signify the semantic and stylistic information of their distributions. After adding noise, these representations are sent back to the server, which uses the pre-trained DM to generate synthetic datasets complying with the client distributions and train a global model on it. With the assistance of vast knowledge within DM, the synthetic datasets have comparable quality and diversity to the client images, subsequently enabling the training of global models that achieve performance equivalent to or even surpassing the ceiling of supervised centralized training. FedDISC works within one communication round, does not require any local training, and involves very minimal information uploading, greatly enhancing its practicality. Extensive experiments on three large-scale datasets demonstrate that FedDISC effectively addresses the semi-FL problem on non-IID clients and outperforms the compared SOTA methods. Sufficient visualization experiments also illustrate that the synthetic dataset generated by FedDISC exhibits comparable diversity and quality to the original client dataset, with a neglectable possibility of leaking privacy-sensitive information of the clients

Remote sensing and optimized neural networks for landslide risk assessment: Paving the way for mitigating Afghanistan landslide damage

Landslides caused by mega earthquakes and other extreme climate change pose a major threat to lives and infrastructure. However, the lack of a detailed and timely landslide inventory and relevant risk assessment attributable to ongoing conflicts limits the effective prevention measures in Afghanistan. This study presents the first landslide inventory covering the whole nation of Afghanistan from 2015 to the present utilizing Google Earth Pro imagery and manual interpretation. Based on this inventory of 3,260 mapped landslides, we analyzed the distributional characteristics of landslides in Afghanistan and conducted a risk assessment that included landslide susceptibility and hazard, and vulnerability of the bearing areas. The existing regional studies attest to the accuracy and reliability of the inventory, and the results of the risk assessment using the optimized neural network method in this study are well validated. This study can provide a good database for the Afghan government to carry out relevant pre-disaster warnings and post-disaster reconstruction, which can help to delineate hotspots where landslides may occur, and reduce potential economic losses and human casualties from future landslides

Collaborative Chinese Text Recognition with Personalized Federated Learning

In Chinese text recognition, to compensate for the insufficient local data and improve the performance of local few-shot character recognition, it is often necessary for one organization to collect a large amount of data from similar organizations. However, due to the natural presence of private information in text data, such as addresses and phone numbers, different organizations are unwilling to share private data. Therefore, it becomes increasingly important to design a privacy-preserving collaborative training framework for the Chinese text recognition task. In this paper, we introduce personalized federated learning (pFL) into the Chinese text recognition task and propose the pFedCR algorithm, which significantly improves the model performance of each client (organization) without sharing private data. Specifically, pFedCR comprises two stages: multiple rounds of global model training stage and the the local personalization stage. During stage 1, an attention mechanism is incorporated into the CRNN model to adapt to various client data distributions. Leveraging inherent character data characteristics, a balanced dataset is created on the server to mitigate character imbalance. In the personalization phase, the global model is fine-tuned for one epoch to create a local model. Parameter averaging between local and global models combines personalized and global feature extraction capabilities. Finally, we fine-tune only the attention layers to enhance its focus on local personalized features. The experimental results on three real-world industrial scenario datasets show that the pFedCR algorithm can improve the performance of local personalized models by about 20\% while also improving their generalization performance on other client data domains. Compared to other state-of-the-art personalized federated learning methods, pFedCR improves performance by 6\% $\sim$ 8\%

U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds

In this paper, we propose U-RED, an Unsupervised shape REtrieval and Deformation pipeline that takes an arbitrary object observation as input, typically captured by RGB images or scans, and jointly retrieves and deforms the geometrically similar CAD models from a pre-established database to tightly match the target. Considering existing methods typically fail to handle noisy partial observations, U-RED is designed to address this issue from two aspects. First, since one partial shape may correspond to multiple potential full shapes, the retrieval method must allow such an ambiguous one-to-many relationship. Thereby U-RED learns to project all possible full shapes of a partial target onto the surface of a unit sphere. Then during inference, each sampling on the sphere will yield a feasible retrieval. Second, since real-world partial observations usually contain noticeable noise, a reliable learned metric that measures the similarity between shapes is necessary for stable retrieval. In U-RED, we design a novel point-wise residual-guided metric that allows noise-robust comparison. Extensive experiments on the synthetic datasets PartNet, ComplementMe and the real-world dataset Scan2CAD demonstrate that U-RED surpasses existing state-of-the-art approaches by 47.3%, 16.7% and 31.6% respectively under Chamfer Distance.Comment: ICCV202

Understanding health education needs of pregnant women in China during public health emergencies: a qualitative study amidst the COVID-19 pandemic

BackgroundPublic health emergencies impose unique challenges on pregnant women, affecting their physiological, psychological, and social wellbeing. This study, focusing on the context of the corona virus disease in 2019 (COVID-19) pandemic in China, aims to comprehensively explore the experiences of pregnant women amidst diverse public health crises. Herein, we investigate the health education needs of pregnant Chinese women in regard to public health emergencies to provide a scientific foundation for the development of targeted health education strategies.ObjectiveThe study described in this article aims to explore the health education needs of pregnant Chinese women in the context of public health emergencies specifying the types of emergencies of pandemics and to provide a scientific basis for targeted health education interventions.MethodsThirteen pregnant women were purposively selected, and the rationale for this sample size lies in the qualitative nature of the study, seeking in-depth insights rather than generalizability. Data collection involved semi-structured interviews, and the Colaizzi, which is a structured qualitative technique used to extract, interpret, and organize significant statements from participant descriptions into themes, providing a comprehensive understanding of their lived experiences.ResultsThe analysis yielded six prominent themes encompassing the following areas: I. Personal protection and vaccine safety; II. Knowledge of maternal health; III. Knowledge of fetal health; IV. Knowledge of childbirth; V. Knowledge of postpartum recovery; and VI. Knowledge sources of health education for pregnant women and their expectations of healthcare providers. Theme I was analyzed with two sub-themes (needs for personal protection knowledge, vaccine safety knowledge needs); Theme II was analyzed with three sub-themes (nutrition and diet, exercise and rest, sexual life); Theme III was analyzed with three sub-themes (medications and hazardous substances, pregnancy check-ups, and fetal movement monitoring); Theme IV was analyzed with three sub-themes (family accompaniment, analgesia in childbirth, and choice of mode of delivery); Theme V was analyzed with one sub-theme (knowledge of postnatal recovery); Theme VI was analyzed with one sub-theme (expectations of Healthcare providers). Sub-themes within each main theme were identified, offering a nuanced understanding of the multifaceted challenges faced by pregnant women during public health emergencies. The interrelation between sub-themes and main themes contributes to a holistic portrayal of their experiences.ConclusionThe study emphasizes the need for healthcare professionals to tailor health education for pregnant women during emergencies, highlighting the role of the Internet in improving information dissemination. It recommends actionable strategies for effective health communication, ensuring these women receive comprehensive support through digital platforms for better health outcomes during public health crises