Federated Learning (FL) is a critical enabler for intelligent Internet of Things (IoT) systems, allowing collaborative model training across heterogeneous devices while preserving data privacy. However, FL performance degrades significantly under non-Independent and Identically Distributed (non-IID) data due to weight divergence, and existing mitigation methods often introduce substantial overhead unsuitable for resource-constrained IoT deployments. We identify that weight divergence in non-IID FL stems primarily from insufficient cross-device knowledge exchange before aggregation—a contributing factor that has been underexplored in existing literature—and propose FedCAD, a communication-efficient FL framework that directly addresses this root cause. The core innovation is delayed cross-group aggregation, which strategically postpones global model updates until each model copy has been sequentially trained across all device groups, ensuring comprehensive knowledge integration. This mechanism is supported by dual-feature device clustering that creates meaningful group structures and fairness-aware adaptive selection that ensures representative participation—forming a causally linked pipeline where clustering provides structure, selection provides quality, and delayed aggregation provides the mechanism for thorough knowledge exchange. Extensive experiments on five benchmark datasets across 13 heterogeneous scenarios demonstrate that FedCAD consistently outperforms ten state-of-the-art methods with negligible additional communication overhead.

Proteins are the material foundation of life, and they are organic macromolecules that make up the basic organic matter of cells. Therefore, proteins can be considered as the main bearers of life activities. Proteins are important components that make up all cells and tissues in an organism. All critical elements of an organism require the participation of proteins, and the most important thing is that they are related to life phenomena. The transportation and localization of proteins within organelles is a complex and delicate process that involves multiple steps and mechanisms. Organelle proteins are an essential element in several biological processions. In this work, we proposed TAPE_selection methods to reduce the useless information of the Tasks Assessing Protein Embed-dings (TAPE) feature in some organelle proteins, which mainly include plant vacuole proteins (PVPs) and peroxidase ones. In order to reduce the useless information, we employed some feature selection Strategies, including the Chi-Squared Test, Minimum Redundancy Maximum Relevance(mRMR), and Neighborhood Components Analysis (NCA). With the selected feature, the Proper Orthogonal Decomposition (POD) and t-distributed Stochastic Neighbor Embedding (t-SNE) were employed to reduce the reconstructed feature scale.

Pneumonia, especially corona virus disease 2019 (COVID-19), can lead to serious acute lung injury, acute respiratory distress syndrome, multiple organ failure and even death. Thus it is an urgent task for developing high-efficiency, low-toxicity and targeted drugs according to pathogenesis of coronavirus. In this paper, a novel disease-related compound identification model-based capsule network (CapsNet) is proposed. According to pneumonia-related keywords, the prescriptions and active components related to the pharmacological mechanism of disease are collected and extracted in order to construct training set. The features of each component are extracted as the input layer of capsule network. CapsNet is trained and utilized to identify the pneumonia-related compounds in Qingre Jiedu injection. The experiment results show that CapsNet can identify disease-related compounds more accurately than SVM, RF, gcForest and forgeNet.

BACKGROUND: The growing researches of molecular biology reveal that complex life phenomena have the ability to demonstrating various types of interactions in the level of genomics. To establish the interactions between genes or proteins and understand the intrinsic mechanisms of biological systems have become an urgent need and study hotspot. RESULTS: In order to forecast gene expression data and identify more accurate gene regulatory network, complex-valued version of ordinary differential equation (CVODE) is proposed in this paper. In order to optimize CVODE model, a complex-valued hybrid evolutionary method based on Grammar-guided genetic programming and complex-valued firefly algorithm is presented. CONCLUSIONS: When tested on three real gene expression datasets from E. coli and Human Cell, the experiment results suggest that CVODE model could improve 20-50% prediction accuracy of gene expression data, which could also infer more true-positive regulatory relationships and less false-positive regulations than ordinary differential equation.