Demystifying and Analysing Metaverse Towards Education 4.0

The word Metaverse has influenced many sectors such as healthcare, education, retail and manufacturing and few more industries are there which will be impacted by 2026 as per the research conducted by Gartner. The word 'Metaverse' especially in education sector came into existence after the COVID-19 epidemic when the humanity were forced to think about the new methodology of educating and teaching. This ecosphere is the combination of technologies which enables multimodal interactions with artificial environment, electronic library and people such as Virtual Reality (VR) and Augmented Reality (AR). It is believed that metaverse will improve collaboration, training process will be enhanced and most importantly it will create a happier workplace. This is only the reason that many corporate giants like Nvidia, facebook, apple, epic Games and companies has shifted towards this pedagogical ecosystem. This technology has the potential which enables absolute incorporating user conversation in actual-time and compelling interactivity with digital artifact. In this paper, we are addressing metaverse in education along with a detailed framework of metaverse in education. It includes a comparative study of conventional education, online education and metaverse education based on parameters like place of learning, resources used, teaching methodology, learning experience, learning target and learning assessment. Competency based education, energize student and positive attitude towards learning. The various challenges of the metaverse in educational sector are also debated. This paper will help the researcher's fraternity to get a deeper insight along with a clear perception of this ecosystem in education. © 2023 IEEE.

Comparative Analysis of Security and Privacy Technique for Federated Learning in IOT Based Devices

The field of machine learning has been seen as a major development in the last few years. Many new algorithms and many new methods have been put forward by various researchers in this domain. Before the COVID-19 pandemic, things were done manually but after this situation, the culture of working from home has been started at almost every organization except few a necessary government organizations which include healthcare and other emergency services. Online work involves a lot of data transfer and hence it is demanded new development in machine learning and this learning emerged as one such development. Federated learning enables multiple devices to build a common machine learning model without sharing data which helps in providing better data privacy because training data are not transmitted to a central server. Federated learning is also known as collective learning where we train the algorithms across various devices with the help of decentralized data samples without the involvement of actual data. In this paper, the authors will provide various use cases, as well as a comparative study of various federated learning frameworks. This paper will provide in-depth knowledge as well as future research directions in the field of federated learning. © 2022 IEEE.