The widespread adoption of machine learning (ML) in privacy-sensitive domains such as healthcare and finance has amplified concerns surrounding data privacy, regulatory compliance, and generalization across data domains. Federated learning (FL) offers partial mitigation by enabling collaborative training without sharing raw data. However, current FL frameworks often fail to address key challenges, including vulnerabilities to semi-honest and colluding participants, inefficiencies in computation and communication for edge devices, and limited support for uncertainty-aware models like Gaussian Processes (GPs). This paper presentsF2GP, a secure federated framework for sparse GP approximation that addresses these gaps through two core innovations. First, a privacy-preserving support set selection algorithm based on one-shot federated$k$-means clustering improves model accuracy with minimal communication overhead. Second, a multi-party homomorphic encryption (HE) scheme secures the aggregation and inference process, ensuring robustness even when up to$M-1$participants collude with the server. We implement F2GP on a testbed of 15 NVIDIA Jetson Nano devices, emulating low-resource edge environments. The framework demonstrates practical scalability and achieves 15–20% better accuracy compared to non-secure distributed and centralized GP baselines, while maintaining sub-linear communication overhead. Evaluations across diverse datasets confirm F2GP's performance, resilient privacy, and suitability for uncertainty-aware inference. The code will be made available onhttps://gitee.com/SZU-AI4H/securedgp

Distributed sparse Gaussian process (dGP) models provide an ability to achieve accurate predictive performance using data from multiple devices in a time efficient and scalable manner. The distributed computation of model, however, risks exposure of privately owned data to public manipulation. In this paper we propose a secure solution for dGP regression models using multi-key homomorphic encryption. Experimental results show that with a little sacrifice in terms of time complexity, we achieve a secure dGP model without deteriorating the predictive performance compared to traditional non-secure dGP models. We also present a practical implementation of the proposed model using several Nvidia Jetson Nano Developer Kit modules to simulate a real-world scenario. Thus, secure dGP model plugs the data security issues of dGP and provide a secure and trustworthy solution for multiple devices to use privately owned data for model computation in a distributed environment availing speed, scalability and robustness of dGP.

Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network’s prediction mechanism must be interpretable to establish trust in the clinical routine. One principal approach to interpretation is feature attribution. Feature attribution methods identify the importance of input features for the output prediction. Building on the Information Bottleneck Attribution (IBA) method, we recognize the RGB’s input regions with high mutual information with the network’s output for each prediction. IBA identifies input regions that have sufficient predictive information. In this paper, we introduce "IBATree", a novel approach that combines IBA with decision trees to enhance both the interpretability and accuracy of DNNs in cancer cell classification. Our method leverages the information bottleneck framework to inject noise into feature maps and then isolates the most informative features for model predictions while maintaining high classification performance. We evaluated our proposed approach on three datasets—CNMC, ISBI2016, and BreaKHis—demonstrating competitive accuracy and producing clear interpretations. This makes IBATree particularly suitable for clinical applications, where understanding the rationale behind predictions is crucial. Our results show that IBATree provides reliable predictions and valuable insights into feature importance, paving the way for its application in various biomedical domains.

The challenge of interpretability remains a significant barrier to adopting deep neural networks in healthcare domains. Although tree regularization aims to align a deep neural network’s decisions with a single axis-aligned decision tree, however, relying on one tree for all inputs often leads to sub-optimal performance and interoperability. To address this limitation, we propose an enhanced tree regularization method that integrates a post-hoc visual explainable model such as Grad-CAM. This approach guides the deep model to be well-approximated by decision trees tailored to the salient regions identified by Grad-CAM in the input space. We rigorously validate the effectiveness of this framework on two cancer cell datasets: CNMC, which focuses on acute lymphoblastic leukemia cells, and ISBI2016, which comprises benign and malignant skin lesions. The results demonstrate that the proposed method delivers simpler and more interpretable explanations without compromising accuracy, thereby advancing the interpretability of deep learning models in critical healthcare applications.