Federated learning has emerged as a widely adopted distributed machine learning paradigm. In the field of medical diagnosis, it has become a research hotspot, enabling multiple institutions to collaboratively leverage medical data for accurate analysis. However, the distributed nature of federated learning also introduces new challenges in data security and privacy protection, i.e., a curious server might collude with the client to infer private data of honest clients. In this paper, we implement our own FHE library and integrate it with several widely used federated learning methods, providing a unified framework. Our framework employs multiple-party Fully Homomorphic Encryption (FHE) to remove the requirements of a trusted third party or central key servers and address data security and privacy concerns in federated learning, ensuring that the data of honest participants is never exposed while maintaining the accuracy of the final analysis. We deploy our Privacy-preserving Federated Learning framework in the context of Melanoma Diagnosis, PFL-MD, and across multiple types of widely used benchmark datasets, our method achieves accuracy comparable to that of the original federated learning framework, thereby enabling reliable melanoma diagnosis. Extensive experiments further demonstrate the effectiveness of our framework.

Deep learning models, due to their strong capabilities in learning from data and representing features, have been widely deployed, particularly in the medical field. However, given the limitations of medical devices and deployment scenarios, more and more researchers are focusing on how to better deploy deep learning models on resource-constrained edge devices to enable real-time medical data analysis. Nevertheless, such resourceconstrained IoT devices present significant challenges, making it difficult to achieve both high accuracy and low inference latency. To address this problem, we propose a novel framework, RC-DLM, designed to split and execute complex deep learning models on IoT devices. Specifically, we partition a deep learning model into multiple sub-models according to the computational capacity of each device, with each sub-model responsible for handling a subset of classes. To further reduce computation overhead and inference latency, we integrate a class-wise pruning method to shrink the size of each sub-model. Through large-scale experiments conducted on four popular datasets with three model architectures, we demonstrate that our approach significantly reduces inference latency and model size by up to 5.72 times and 57.5 times, respectively. We further deploy our method on real-world edge devices and compare it with state-of-theart approaches, evaluating the three most important aspects: accuracy, inference time, and model size. The comprehensive experimental results of our RC-DLM confirm the effectiveness of our proposed method.

In regions with limited medical resources, detecting melanoma has long been a challenging problem, particularly due to its heterogeneity and rarity. However, with the recent advances in deep learning technologies, it has become increasingly feasible to enable melanoma detection in remote areas. By collecting melanoma datasets, researchers have focused on exploring more effective models to learn discriminative features and deploy automated detection systems, with the aim of assisting doctors in remote or under-resourced regions. In this paper, we explore the latest deep learning model, Vision Mamba, as an AI-based diagnostic approach for melanoma detection. Furthermore, we enhance the Vision Mamba model by analyzing the differences between benchmark datasets and real-world clinical data, aiming to further reduce the false positive rate and minimize misdiagnosis. We conduct experiments on multiple types of widely used datasets across three model architectures and compare our approach against several popular baseline methods. Extensive experimental results demonstrate that Vision Mamba achieves state-of-the-art performance and validates its effectiveness for melanoma diagnosis in diverse clinical scenarios.

Variable selection for genome-wide association studies (GWAS) has long been a central focus in academic research. However, with the advent of the big data era and the rapid growth of biomedical and healthcare data, scientists are increasingly challenged to extract meaningful information from massive datasets. Worse still, such data are often distributed across multiple parties, making collaborative analysis necessary while also requiring strong privacy preservation. To date, there is still no effective framework that can support high-dimensional data analysis, ensure data privacy across collaborators, and simultaneously capture the relatedness between explanatory and response variables. To address these challenges, we introduce the first high-performance computing framework for variable selection in GWAS with Vertical Federated Learning, termed VS-VFL. Our approach leverages Vertical Federated Learning to enable seamless multi-party collaboration while maintaining data privacy and security. Furthermore, we integrate a wide range of state-of-the-art methods, allowing collaborators to apply their preferred techniques, and we explicitly account for the noni.i.d. nature of biomedical data when analyzing the relatedness between explanatory and response variables. In addition, we employ novel optimization strategies and adaptive algorithms to efficiently handle high-dimensional data with sparse features. This framework empowers researchers to conduct comprehensive analyses and perform accurate linkage mapping of gene associations. Our framework is implemented in Python, can be easily deployed on any platform, and is designed to make advanced GWAS analysis accessible to a broader community of researchers.

Variable selection for genome-wide association studies (GWAS) has been a major research focus for decades. With the exponential growth of biological and biomedical data in the era of big data, scientists are confronted with the challenge of extracting meaningful information from vast datasets while managing the inherent heterogeneity in bioinformatics. To date, there are no highly effective tools that support high-dimensional datasets and achieve robust variable selection performance, all while accounting for the non-i.i.d. features and structured relatedness among explanatory and response variables.To address these challenges, we introduce the first high-performance computing framework for variable selection in GWAS. Our framework integrates various state-of-the-art methods, allowing researchers to easily combine different techniques and fully explore their potential. Additionally, our approach employs novel optimization strategies to solve the problem efficiently, even for high-dimensional data with sparse characteristics. By processing the data holistically, the framework delivers comprehensive analysis and accurate linkage mapping associations. Designed for ease of use, the framework is implemented in Python and offers seamless deployment, making it accessible to a wide range of researchers.

Variable selection for genome-wide association studies is a key focus for bioinformatics researchers in high-performance computing. The rapid growth of biological and biomedical data demands has led to high-dimensional, heterogeneous datasets characterized by non-i.i.d. properties and numerous response variables, often resulting in false negatives or positives in recovered results. Traditional methods, when nal̈ively applied, yield suboptimal performance due to confounding factors. To account for the complex interdependencies in heterogeneous data and enhance the practical outcomes of genome-wide association studies, we introduce two methods, TGsLMM and TTsLMM, which balance effects between response and explanatory variables for subpopulation inference. Our unified framework performs sparse variable selection using graph-structured or tree-guided structures in a low-rank linear mixed model. Additionally, we extend our approach to high-dimensional datasets and adaptively select the covariance structure for genomic data. Extensive experiments on synthetic and three real-world datasets emphasize the robustness and effectiveness of our proposed methods, achieving the highest ROC area compared to baselines and superior results for future potential.

The integration of 5G/6G networks with intelligent healthcare systems has enabled early disease detection through patient data monitoring. However, the Internet of Medical Things (IoMT) and remote healthcare services introduce significant privacy and security risks. In this paper, we propose LiteCrypt, which addresses these challenges by introducing an optimized Homomorphic Convolutional Neural Networks (HCNN) structure for secure inference and a lightweight Threshold Signature Scheme (TSS) based dual-authorization mechanism. To enhance the practicality of Homomorphic Encryption (HE)-based secure inference in telemedicine applications, LiteCrypt presents an optimized HCNN framework that ensures efficient and adaptable operations across multiple datasets. A high-performance GPU-accelerated HE engine is developed to address the computational demands of HE operations, enabling real-time processing of encrypted patient data. Besides, LiteCrypt introduces a novel TSS-based dual-authorization protocol, requiring consent from both the patient and the hospital to access patient data, thereby mitigating unauthorized access risks. The system adapts to a flexible 2-out-of-3 authorization scheme for emergencies, ensuring timely data retrieval while maintaining security. To overcome the initial challenge of prolonged computation time due to compute-intensive operations, In LiteCrypt, we utilized the lightweight TSS protocol, based on Oblivious Transfer (OT), which is designed for resource-constrained IoMT devices, reducing computation time from 11.9 to 0.11 seconds. Empirical validation demonstrates LiteCrypt’s superior performance, achieving a 233-fold increase in processing speed, a $96 \%$ reduction in encrypted message size, and a 28-fold speed increase using GPUs.

Executing deep neural networks for inference on the server-class or cloud backend based on the data generated at the edge of the Internet of Things is desirable due primarily to the limited compute power of the edge devices and the need to protect the confidentiality of the inference neural networks. However, such a remote inference scheme incurs concerns regarding the privacy of the inference data transmitted by the edge devices to the curious backend. This article presents a lightweight and unobtrusive approach to obfuscate the inference data at the edge devices. It is lightweight in that the edge device only needs to execute a small-scale neural network; it is unobtrusive in that the edge device does not need to indicate whether obfuscation is applied. Extensive evaluation by three case studies of free-spoken digit recognition, handwritten digit recognition, and American sign language recognition shows that our approach effectively protects the confidentiality of the raw forms of the inference data while effectively preserving backend's inference accuracy.