A Comprehensive Overview of IoT-Based Federated Learning: Focusing on Client Selection Methods.

The integration of the Internet of Things (IoT) with machine learning (ML) is revolutionizing how services and applications impact our daily lives. In traditional ML methods, data are collected and processed centrally. However, modern IoT networks face challenges in implementing this approach due to their vast amount of data and privacy concerns. To overcome these issues, federated learning (FL) has emerged as a solution. FL allows ML methods to achieve collaborative training by transferring model parameters instead of client data. One of the significant challenges of federated learning is that IoT devices as clients usually have different computation and communication capacities in a dynamic environment. At the same time, their network availability is unstable, and their data quality varies. To achieve high-quality federated learning and handle these challenges, designing the proper client selection process and methods are essential, which involves selecting suitable clients from the candidates. This study presents a comprehensive systematic literature review (SLR) that focuses on the challenges of client selection (CS) in the context of federated learning (FL). The objective of this SLR is to facilitate future research and development of CS methods in FL. Additionally, a detailed and in-depth overview of the CS process is provided, encompassing its abstract implementation and essential characteristics. This comprehensive presentation enables the application of CS in diverse domains. Furthermore, various CS methods are thoroughly categorized and explained based on their key characteristics and their ability to address specific challenges. This categorization offers valuable insights into the current state of the literature while also providing a roadmap for prospective investigations in this area of research.

The prediction of mortality influential variables in an intensive care unit: a case study.

The intensive care units (ICUs) are among the most expensive and essential parts of all hospitals for extremely ill patients. This study aims to predict mortality and explore the crucial factors affecting it. Generally, in the health care systems, having a fast and precise ICU mortality prediction for patients plays a key role in care quality, resulting in reduced costs and improved survival chances of the patients. In this study, we used a medical dataset, including patients' demographic details, underlying diseases, laboratory disorder, and LOS. Since accurate estimates are required to have optimal results, various data pre-processings as the initial steps are used here. Besides, machine learning models are employed to predict the risk of mortality ICU discharge. For AdaBoost model, these measures are considered AUC= 0.966, sensitivity (recall) = 87.88%, Kappa=0.859, F-measure = 89.23% making it, AdaBoost, accounts for the highest rate. Our model outperforms other comparison models by using various scenarios of data processing. The obtained results demonstrate that the high mortality can be caused by underlying diseases such as diabetes mellitus and high blood pressure, moderate Pulmonary Embolism Wells Score risk, platelet blood count less than 100000 (mcl), hypertension (HTN), high level of Bilirubin, smoking, and GCS level between 6 and 9.

Extract critical factors affecting the length of hospital stay of pneumonia patient by data mining (case study: an Iranian hospital).

MOTIVATION: Pneumonia is a prevalent infection of lower respiratory tract caused by infected lungs. Length of stay (LOS) in hospital is one of the simplest and most important indicators in hospital activity that is used for different purposes. The aim of this study is to explore the important factors affecting the LOS of patients with pneumonia in hospitals. METHODS: The clinical data set for the study were collected from 387 patients in a specialized hospital in Iran between 2009 and 2015. Patients discharge summary includes their demographic details, reasons for admission, prescribed medications for the patient, the result of laboratory tests, and length of treatment. RESULTS AND CONCLUSIONS: The proposed model in the study demonstrates the way various scenarios of data processing impact on the scale efficiency model, which points to the significance of the pre-processing in data mining. In this article, some methods were utilized; it is noteworthy that Bayesian boosting method led to better results in identifying the factors affecting LOS (accuracy 95.17%). In addition, it was found that 58% of patients younger than 15 years old and 74% of the elderly within the age range of 74-88 were more vulnerable to pneumonia disease. Also, it was found that the Meropenem is a relatively more effective medicine compared to other antibiotics which are used to treat pneumonia in the majority of age groups. Regardless of the impact of various laboratory findings (including CRP, ESR, WBC, NA, K), the patients LOS decreased as a result of Meropenem.