Muhammad Zulkifl Hasan

The federated learning (FL) concept makes sure that a machine learning model is created by multiple clients without them sharing their local data. The prohibition policies of data exchange among organizations provided by various governments have resulted in the necessity of privacy-preserved federated learning. Federated learning has developed this concept of modeling development in many industries to improve performance and accuracy. The following paper provides the classification of Federate Learning, covering the presence of aggregation algorithms, frameworks, aspects of implementation, and dataset repositories, making it a crucial source of information to researchers working in the area. The paper critically examines the available centralized and decentralized FL methods that have been proposed in the literature and provides an overview regarding the methodology, privacy techniques applied and constraints to inform other researchers in making improvements in the area of federated learning. The article addresses the importance of privacy-enhancing technology, such as differential privacy (DP), homomorphic encryption (HE), and secure multiparty computation (SMPC) in federated learning as they can achieve privacy as well as effectiveness in terms of communication efficiency and the cost of computation. The article examines federated learning applications in the privacy-sensitive field of natural language processing (NLP) and healthcare, as well as in the Internet of Things (IoT) and edge computing. We think that our contribution is a new one because it determines privacy evaluation metrics and highlights the measures according to the data privacy and correctness, cost of communication, cost of computation and scalability. Moreover, it presents an emergent need and offers prospective research areas of the federated learning field.

Prior studies on behaviour-based threat detection on Internet of Things (IoT) device networks have generated machine-learning models with a limited and frequently unproven capacity to learn from unseen data. In this study, we provide a generalizability-focused modelling technique for IoT network assaults that also improves detection and performance. Firstly, we develop a multi-step feature selection technique that minimizes overfitting and provides an enhanced rolling window strategy for feature extraction. Second, in order to prevent frequent data leaks that have restricted the generalizability of earlier models, we develop and test our models using separate train and test datasets. Third, we employ a wide range of machine learning models, assessment measures, and datasets to assess our approach thoroughly. Lastly, we employ explainable AI approaches to strengthen the models' confidence, enabling us to pinpoint the characteristics that support precise attack detection. Models are updated gradually by use of algorithms such as Online Naive Bayes and Stochastic Gradient Descent (SGD).

HRMARS is well established journal publisher based in Pakistan. It is publishing prominent journals in social sciences since 2011.

The emergence of Internet of Things (IoT) technologies has led to the development of smart cities, potentially improving urban life through efficient and effective use of resources. The success of IoT-based applications in smart cities is contingent on several factors, including network power challenges. Deploying IoT-based applications in smart cities requires a reliable and efficient network infrastructure to ensure smooth and uninterrupted data communication. One of the significant challenges faced in this regard is low network power, which can significantly impact the performance of IoT devices and, consequently, the entire smart city system. This article addresses the issue of low network power in IoT-based applications in smart cities. It investigates the causes of this problem and the potential solutions that can be adopted to mitigate its impact. The article also highlights the importance of network power management in the context of IoT-based applications in smart cities. By implementing effective network power management techniques, it is possible to optimize the performance of IoT devices and extend their battery life, thus ensuring the overall efficiency and sustainability of the smart city system. The article concludes by emphasizing the need for continuous research and development to overcome the challenges of low network power and further enhance IoT technology's capabilities in smart cities.