STRUCTURING FEDERATED LEARNING APPLICATIONS – A LITERATURE ANALYSIS AND TAXONOMY

Ensuring data privacy is an essential objective competing with the ever-rising capabilities of machine learning approaches fueled by vast amounts of centralized data. Federated learning addresses this conflict by moving the model to the data while ensuring that the data itself does not leave a client\u27s device. However, maintaining privacy impels new challenges concerning algorithm performance or fairness of the algorithm\u27s results that remain uncovered from a sociotechnical perspective. We tackle this research gap by conducting a structured literature review and analyzing 152 articles to develop a taxonomy of federated learning applications consisting of nine dimensions and 25 characteristics. Our taxonomy illustrates how different attributes of federated learning affect trade-offs between an algorithm\u27s privacy, performance, and fairness. Despite an increasing interest in the technical implementation of federated learning, our work is one of the first to emphasize an information systems perspective on this emerging and promising topic

MOF materials as therapeutic agents, drug carriers, imaging agents and biosensors in cancer biomedicine:Recent advances and perspectives

We summarize recent advances in application of MOFs as therapeutic agents, drug carriers, imaging agents and biosensors in cancer biomedicine. A holistic perspective is adopted to produce a comprehensive, critical and readable document useful to a broad community in chemistry, material science, medical fields etc. None of the previous articles adopted a holistic approach focusing on a specific disease or area, such as cancer. MOFs have a tremendous potential in cancer diagnostics and treatment. Although a new field, the amount of literature and data accumulated in this area is vast, quickly growing and requires some systematization and processing. We propose a broad overview of MOF-related literature in the treatment and diagnosis of cancer. In our study, we set: (i) to consolidate the most important and up to date information from the field of MOFs applications in medicine, particularly in anticancer therapy; and to reflect these developments in one, comprehensive study, (ii) to highlight new and emerging topics in the field, (iii) to tabulate the large number of the application examples and case studies to make the information more accessible and easy to follow, (iv) and finally, to broadly reflect on the potential of MOFs in application to cancer treatment, including the existing challenges and emerging opportunities.</p

ECIS 2023 - STRUCTURING FEDERATED LEARNING APPLICATIONS – A LITERATURE ANALYSIS AND TAXONOMY

   Ensuring data privacy is an essential objective competing with the ever-rising capabilities of machine learning approaches fueled by vast amounts of centralized data. Federated learning addresses this conflict by moving the model to the data and ensuring the data itself does not leave a client's device. However, maintaining privacy impels new challenges concerning algorithm performance or fairness of the algorithm's results that remain uncovered from a sociotechnical perspective. We tackle this research gap by conducting a structured literature review and analyzing 152 articles to develop a taxonomy of federated learning applications with nine dimensions and 24 characteristics. Our taxonomy illustrates how different attributes of federated learning may affect the trade-off between an algorithm's privacy, performance, and fairness. Despite an increasing interest in the technical implementation of federated learning, our work is one of the first to emphasize an information systems perspective on this emerging and promising topic.</p

Generation schemes for the resource-constrained project scheduling problem with partially renewable resources and generalized precedence constraints

In recent years, new resource types have been established in project scheduling. These include so-called partially renewable resources, whose total capacity applies only to a subset of periods in the planning horizon. In this paper, we consider the extension of the resource-constrained project scheduling problem with those partially renewable resources as well as generalized precedence constraints with the objective of minimizing the project duration (RCPSP/max- π). For this problem it is known that already the determination of a feasible solution is NP-hard in the strong sense. Hence, we present two different generation schemes that are able to find good feasible solutions in short time for most tested instances. The first one is a construction-based heuristic wherein the activities of the project are scheduled iteratively time- and resource-feasibly. The second one is a relaxation-based generation scheme, in which—starting from the schedule consisting of the earliest start times—resource conflicts are identified and resolved by inserting additional resource constraints