Many algorithms use clustering to improve vehicular ad hoc network performance. The expected points of many of these approaches support multiple rounds of data to the roadside unit and constantly include clustering in every round of single-hop data transmission towards the road side unit; however, the clustering in every round maximizes the number of control messages and there could be the possibility of collision and decreases in network energy. Multi-hop transmission prolongs the cluster head node’s lifetime and boosts the network’s efficiency. Accordingly, this article proposes a new fuzzy-clustering-based routing algorithm to benefit from multi-hop transmission clustering simultaneously. This research has analyzed the limitation of clustering in each round, different algorithms were used to perform the clustering, and multi-hop routing was used to transfer the data of every cluster to the road side unit. The fuzzy logic was used to choose the head node of each cluster. Three parameters, (1) distance of each node, (2) remaining energy, and (3) number of neighbors of every node, were considered as fuzzy criteria. The results of this research were compared to various other algorithms in relation to parameters like dead node in every round, first node expire, half node expire, last node expire, and the network lifetime. The simulation results show that the proposed approach outperforms other methods. On the other hand, the vehicular ad hoc network (VANET) environment is vulnerable at the time of data transmission. The NS-2 software tool was used to simulate and evaluate the proposed fuzzy logic opportunistic routing’s performance results concerning end-to-end delay, packet delivery, and network throughput. We compare to the existing protocols, such as fuzzy Internet of Things (IoT), two fuzzy, and Fuzzy-Based Driver Monitoring System (FDMS). The performance comparison also emphasizes an effective utilization of the resources. Simulations on the highway environment show that the suggested protocol has an improved Quality of Service (QoS) efficiency compared to the above published methods in the literature.

The Internet of Medical Things (IoMT) can automate breast tumor detection and classification with the potential of artificial intelligence. However, the leakage of sensitive data can cause harm to patients. To address this issue, this study proposed an intrauterine breast cancer diagnosis method, namely “Privacy-Embedded Lightweight and Efficient Automated (PLA)”, for IoMT, which represents an approach that combines privacy-preserving techniques, efficiency, and automation to achieve our goals. Firstly, our model is designed to achieve lightweight classification prediction and global information processing of breast cancer by utilizing an advanced IoMT-friendly ViT backbone. Secondly, PLA protects patients’ privacy by federated learning, taking the classification task of breast cancer as the main task and introducing the texture analysis task of breast cancer images as the auxiliary task to train the model. For our PLA framework, the classification accuracy is 0.953, the recall rate is 0.998 for the best, the F1 value is 0.969, the precision value is 0.988, and the classification time is 61.9 ms. The experimental results show that the PLA model performs better than all of the comparison methods in terms of accuracy, with an improvement of more than 0.5%. Furthermore, our proposed model demonstrates significant advantages over the comparison methods regarding time and memory.

Ovarian cancer remains a significant global health concern, and its diagnosis heavily relies on whole-slide images (WSIs). Due to their gigapixel spatial resolution, WSIs must be split into patches and are usually modeled via multi-instance learning (MIL). Although previous studies have achieved remarkable performance comparable to that of humans, in clinical practice WSIs are distributed across multiple hospitals with strict privacy restrictions, necessitating secure, efficient, and effective federated MIL. Moreover, heterogeneous data distributions across hospitals lead to model heterogeneity, requiring a framework flexible to both data and model variations. This paper introduces HFed-MIL, a heterogeneous federated MIL framework that leverages gradient-based attention distillation to tackle these challenges. Specifically, we extend the intuition of Grad-CAM to the patch level and propose Patch-CAM, which computes gradient-based attention scores for each patch embedding, enabling structural knowledge distillation without explicit attention modules while minimizing privacy leakage. Beyond conventional logit distillation, we designed a dual-level objective that enforces both class-level and structural-level consistency, preventing the vanishing effect of naive averaging and enhancing the discriminative power and interpretability of the global model. Importantly, Patch-CAM scores provide a balanced solution between privacy, efficiency, and heterogeneity: they contain sufficient information for effective distillation (with minimal membership inference risk, MIA AUC ≈ 0.6) while significantly reducing communication cost (0.32 MB per round), making HFed-MIL practical for real-world federated pathology. Extensive experiments on multiple cancer subtypes and cross-domain datasets (Camelyon16, BreakHis) demonstrate that HFed-MIL achieves state-of-the-art performance with enhanced robustness under heterogeneity conditions. Moreover, the global attention visualizations yield sharper and clinically meaningful heatmaps, offering pathologists transparent insights into model decisions. By jointly balancing privacy, efficiency, and interpretability, HFed-MIL improves the practicality and trustworthiness of deep learning for ovarian cancer WSI analysis, thereby increasing its clinical significance.

The adoption of artificial intelligence (AI) in healthcare requires not only accurate predictions but also a clear understanding of its reliability. In safety-critical domains such as medical imaging and diagnosis, clinicians must assess the confidence in model outputs to ensure safe decision making. Uncertainty quantification (UQ) addresses this need by providing confidence estimates and identifying situations in which models may fail. Such uncertainty estimates enable risk-aware deployment, improve model robustness, and ultimately strengthen clinical trust. Although prior studies have surveyed UQ in centralized learning, a systematic review in the federated learning (FL) context is still lacking. As a privacy-preserving collaborative paradigm, FL enables institutions to jointly train models without sharing raw patient data. However, compared with centralized learning, FL introduces more complex sources of uncertainty. In addition to data uncertainty caused by noisy inputs and model uncertainty from distributed optimization, there also exists distributional uncertainty arising from client heterogeneity and personalized uncertainty associated with site-specific biases. These intertwined uncertainties complicate model reliability and highlight the urgent need for UQ strategies tailored to federated settings. This survey reviews UQ in medical FL. We categorize uncertainties unique to FL and compare them with those in centralized learning. We examine the sources of uncertainty, existing FL architectures, UQ methods, and their integration with privacy-preserving techniques, and we analyze their advantages, limitations, and trade-offs. Finally, we highlight key challenges—scalable UQ under non-IID conditions, federated OOD detection, and clinical validation—and outline future opportunities such as hybrid UQ strategies and personalization. By combining methodological advances in UQ with application perspectives, this survey provides a structured overview to inform the development of more reliable and privacy-preserving FL systems in healthcare.