Federated learning (FL) and artificial intelligence (AI) are the two most cutting-edge and well-liked technologies in the field of intelligent healthcare. The medical industry has depended on centralized agents sharing data that has not been processed. Therefore, this method still has a lot of problems and flaws. FL is the process of training statistical models on mobile devices from a central location using decentralized data while keeping the privacy of the devices safe. Privacy-preserving FL methods have a lot to offer to learning-based healthcare systems, especially in the light of patient privacy requirements. This manuscript used the PRISMA guideline to conduct a systematic review. The goal of the paper is to give a thorough understanding of the architecture and difficulties associated with FL’s growing use in the healthcare sector. Publications related to FL in healthcare were gathered, categorized, and examined using a systematic review methodology. A total of 186 papers were selected from IEEE, Elsevier, Arxiv, ACM, MDPI, and WOS databases. 38 duplicates were removed during the identification phase. Furthermore, 49 publications were excluded after assessing their titles and keywords. Twenty-three of the 99 papers that satisfied the full-text eligibility criteria were excluded due to insufficient relevance and inadequate focus on quantitative evaluations. Consequently, 76 papers were chosen for full-text review. The terms "FL", "Healthcare", and "aggregation" were used in the English-language publication of the inclusion measures. The paper aims to give a thorough analysis of the architecture, aggregation techniques, and difficulties associated with the growing usage of FL in the healthcare industry. We discuss the aggregation techniques, implementation frameworks, and design to analyze and assess FL’s capabilities along with robust aggregation approaches. However, the aggregation technique selected determines the final global model’s quality and dependability; therefore, choosing the correct method is essential. A thorough grasp of the various model aggregation strategies and their advantages and disadvantages is necessary, as FL becomes more prevalent across multiple disciplines. Additionally, we thoroughly examined FL’s applicability in the healthcare sector, because in the FL model, training occurs at the client site only. FL excels in protecting privacy because it does not require centralized data aggregation, which presents serious privacy problems. Researchers may use this survey to improve and create novel aggregation methods, while practitioners can use it to choose the best approach for their FL applications.

The necessity to ensure that Internet of Things (IoT) networks are secure is one of the biggest issues that has arisen as a result of the growing demand for technology that uses the IoT. Considering how many gadgets are linked to the internet, safeguarding their networks is a growing worry. Due to the IoT's network's complexity and resource constraints, traditional intrusion detection systems encounter a number of problems. The main objectives of this project are to design, develop, and evaluate a hybrid level placement method for an IDS based on multi- agent systems, BC technology (Block-Chain), and DL algorithms (Deep Learning). The breakdown of data administration, data collection, analysis, and reaction into its component parts reveals the overall system design. The National Security Laboratory's knowledge discovery and data mining dataset is used to test the system as part of the validation procedure. These results demonstrate how deep learning algorithms are effective at identifying risks at the network and transport levels. The experiment shows that deep learning techniques function well when used to find intrusions in a network environment for the Internet of Things.