Trustworthy Federated Learning: A Survey

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.Comment: 45 Pages, 8 Figures, 9 Table

Towards a national trauma registry for the United Arab Emirates

<p>Abstract</p> <p>Background</p> <p>Trauma is a major health problem in the United Arab Emirates (UAE) as well as worldwide. Trauma registries provide large longitudinal databases for analysis and policy improvement. We aim in this paper to report on the development and evolution of a national trauma registry using a staged approach by developing a single-center registry, a two-center registry, and then a multi-center registry. The three registries were established by developing suitable data collection forms, databases, and interfaces to these databases. The first two registries collected data for a finite period of time and the third is underway. The steps taken to establish these registries depend on whether the registry is intended as a single-center or multi-center registry.</p> <p>Findings</p> <p>Several issues arose and were resolved during the development of these registries such as the relational design of the database, whether to use a standalone database management system or a web-based system, and the usability and security of the system. The inclusion of preventive medicine data elements is important in a trauma registry and the focus on road traffic collision data elements is essential in a country such as the UAE. The first two registries provided valuable data which has been analyzed and published.</p> <p>Conclusions</p> <p>The main factors leading to the successful establishment of a multi-center trauma registry are the development of a concise data entry form, development of a user-friendly secure web-based database system, the availability of a computer and Internet connection in each data collection center, funded data entry personnel well trained in extracting medical data from the medical record and entering it into the computer, and experienced personnel in trauma injuries and data analysis to continuously maintain and analyze the registry.</p