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Federation analytics approaches are a present area of study that has already progressed beyond the analysis of metrics and counts. It is possible to acquire aggregated information about on-device data by training machine learning models using federated learning techniques without any of the raw data ever having to leave the devices in the issue. Web browser forensics research has been focused on individual Web browsers or architectural analysis of specific log files rather than on broad topics. This paper aims to propose major tools used for Web browser analysis.

Each kind of Web browser has its own unique set of features. This allows the user to choose their preferred browsers or to check out many browsers at once. If a forensic examiner has access to just one Web browser's log files, he/she makes it difficult to determine which sites a person has visited. The agent must thus be capable of analyzing all currently available Web browsers on a single workstation and doing an integrated study of various Web browsers.

Federated learning has emerged as a training paradigm in such settings. Web browser forensics research in general has focused on certain browsers or the computational modeling of specific log files. Internet users engage in a wide range of activities using an internet browser, such as searching for information and sending e-mails.

It is also essential that the investigator have access to user activity when conducting an inquiry. This data, which may be used to assess information retrieval activities, is very critical. In this paper, the authors purposed a major tool used for Web browser analysis. This study's proposed algorithm is capable of protecting data privacy effectively in real-world experiments.

Industry 4.0 implies that global challenges exist within the manufacturing sector. Both theoretical and empirical research has been developed to support these transformations and assist companies in the process of changing. The purpose of this paper is to gather previous articles through an updated review and defines a research agenda for future investigation based on the most recent studies published in the field.

Key articles on the subject are analysed. The articles were published in 39 journals from which 107 papers dating from 2005 to 2018 have been selected.

The main findings imply the definition of a research agenda where: a common terminology should be created; the levels of implementation of Industry 4.0 should be defined; the stages of the development of Industry 4.0 should be identified; a lean approach for this industry is defined and the implications of Industry 4.0 in either a sustainable or circular economy should be understood; the consequences of human resources should be analysed; and the effects of the smart factory in the organisation are the areas identified and studied in the mentioned research agenda.

This review has some limitations. First, a number of grey literature, such as reports from non-governmental organisations and front-line practitioners’ reflections, were not included. Second, only research studies in English and Spanish were reviewed.

This review helps practitioners in their implementation of Industry 4.0. Moreover, the identified future research areas may help to define priorities in this implementation.

After examining previous research, this paper proposes a research agenda covering issues about Industry 4.0. This research agenda should guide future investigations in the smart industry.