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Artificial intelligence employs Machine Learning (ML) and Deep Learning (DL) to analyze data. In both, the data is stored centrally. The data involved may be sensitive and leakage may incur consequences. Applications dealing with intimate data, with critical results, cannot afford this risk and are termed Data-Sensitive Applications (DSA). Some examples are healthcare, finance, etc. The data required for DSA cannot be stored centrally due to large amounts, or isolated data islands. The ML and DL techniques following a data-centralized approach have difficulties in handling the scattered data frequently associated with DSA. Federated Learning (FL) acknowledges the scattered data and provides a more secure and efficient way to analyze such data. This motivates previously reluctant entities like banks to collaborate for variety and quantity of data. Most DSA transitioned to FL, but the migration is not without concerns. These include communication costs, heterogeneity, and malicious attacks. In this paper, we deeply analyze the role of FL in DSA and provide a taxonomy for the studies and implementations of FL. Then we provide an insight into DSA covering works in healthcare and finance. A glance is provided at attempts in non-DSA with possible DSA applications. Finally, we discuss FL's open issues and challenges with their possible solutions. [ABSTRACT FROM AUTHOR]

Artificial intelligence employs Machine Learning (ML) and Deep Learning (DL) to analyze data. In both, the data is stored centrally. The data involved may be sensitive and leakage may incur consequences. Applications dealing with intimate data, with critical results, cannot afford this risk and are termed Data-Sensitive Applications (DSA). Some examples are healthcare, finance, etc. The data required for DSA cannot be stored centrally due to large amounts, or isolated data islands. The ML and DL techniques following a data-centralized approach have difficulties in handling the scattered data frequently associated with DSA. Federated Learning (FL) acknowledges the scattered data and provides a more secure and efficient way to analyze such data. This motivates previously reluctant entities like banks to collaborate for variety and quantity of data. Most DSA transitioned to FL, but the migration is not without concerns. These include communication costs, heterogeneity, and malicious attacks. In this paper, we deeply analyze the role of FL in DSA and provide a taxonomy for the studies and implementations of FL. Then we provide an insight into DSA covering works in healthcare and finance. A glance is provided at attempts in non-DSA with possible DSA applications. Finally, we discuss FL's open issues and challenges with their possible solutions. [ABSTRACT FROM AUTHOR]

Utilizing cutting-edge technology in the healthcare industry could provide advanced methods for managing medical records for patients' treatment. Integrating essential technologies like artificial intelligence (AI) can enhance healthcare services. The intersection of the Internet of Things (IoT) and AI presents various prospects within the healthcare industry. This research paper presents a novel model for enhancing the standard of treatment in smart healthcare systems (SHS) based on the convergence of AI and IoT. We investigate optimizing patient data processing performance in smart healthcare centers. The upgraded particle swarm optimization-long short-term memory (PSO-LSTM) algorithm is introduced to optimize the IoT-based SHS model. In order to perform a more suitable classification of the patient medical data, PSO is determined to be compared with PSO-LSTM to tune several metrics and benchmarks to achieve the highest value on patient data process performance. The proposed outcomes discovered that in terms of the test sets, the predicted values of patient health risks, and just six of them are varied, with an accuracy of 92.5%. This accuracy indicates the PSO-LSTM algorithm has more satisfactory performance and higher efficiency and provides a more secure, reliable, and improved overall patient satisfaction experience. [ABSTRACT FROM AUTHOR]

Internet of Things is shaping the quality of living standard. With the rapid growth and expansion of adopting IoT-based approaches, their security represents a growing challenge for both manufacturers and consumers. There is a recent rising trend towards employing artificial intelligence approaches to enhance the security of IoT infrastructure. This survey paper focuses on reviewing recent developments in applying artificial intelligence to intrusion detection in the IoT domain. Selected articles are classified according to the applied AI algorithm. This study provides an in-depth survey highlighting the recent advances in artificial intelligence for improving the security of IoT. It summarizes and organizes the recent related research, then presents a comprehensive discussion on research challenges, open issues, and needed future research. [ABSTRACT FROM AUTHOR]