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

Connected Blockchain Federations for Sharing Electronic Health Records

With the growing utility of blockchain technology, the desire for reciprocal interactions among different blockchains is growing. However, most operational blockchain networks currently operate in a standalone setting. This fragmentation in the form of isolated blockchains creates interoperability difficulties, inhibiting the adoption of blockchains in various ecosystems. Interoperability is a key factor in the healthcare domain for sharing EHRs of patients registered in independent blockchain networks. Each blockchain network could have its own rules and regulations, obstructing the exchange of EHRs for improving diagnosis and treatments. Examples include patients being treated by healthcare providers in different countries or regions, or within one country but with a different set of rules per state or emirate. By contrast, a federation of blockchain networks can provide better communication and service to stakeholders in healthcare. Thus, solutions for facilitating inter-blockchain communication in such a blockchain federation are needed. However, this possibility has not been fully explored, and further investigations are still being conducted. Hence, the present study proposes a transaction-based smart contract triggering system for inter-blockchain communication, enabling EHR sharing among independent blockchains. We use local and global smart contracts that will be executed once a transaction is created in the blockchain. Local smart contracts are used for EHR sharing within the blockchain, whereas global smart contracts are used for EHR sharing among independent blockchains. The experimental setup is conducted using the Hyperledger Fabric blockchain platform. Inter-blockchain communication between two independent fabric networks is conducted through a global smart contract using Hyperledger Cactus for EHR sharing in a health federation setup. To the best of our knowledge, our study is the first to implement an inter-blockchain communication model in the healthcare domain

Web based GIS for buildings rent prices in Abu Dhabi

GIS is one of the important sciences in these years. This highlights the importance of spatial factor in decision making in real estate. The purpose of this study is to use GIS to find house to rent simply by applying inquires and to find satisfaction locations. This study focuses on the rent prices in Abu-Dhabi Island. By using web-page browsing, it will help to choose a house that has the biggest weight and ranked the entire selections form most likely match to lowest match. So the user can see the report and select the optimal case that matches their needs. A list of selected range price will be shown to the user to select. A map with colored symbols according to the prices will be generated, and also it shows the targeted buildings that match the user needs. Then the user can choose and judge for the most advantageous case for them. The user can search by price, buildings name, apartment size and number of rooms. This project is one of the GIS applications that could help people in searching for new apartment to live in. For other people such as brokers, they could promote their buildings in the system

A Study on Predicting the Outbreak of COVID-19 in the United Arab Emirates: A Monte Carlo Simulation Approach

According to the World Health Organization updates, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a pandemic between 2019 and 2022, with millions of confirmed cases and deaths worldwide. There are various approaches to predicting the suspected, infected, and recovered (SIR) cases with different factual or epidemiological models. Some of the recent approaches to predicting the COVID-19 outbreak have had positive impacts in specific nations. Results show that the SIR model is a significant tool to cast the dynamics and predictions of the COVID-19 outbreak compared to other epidemic models. In this paper, we employ the Monte Carlo simulation to predict the spread of COVID-19 in the United Arab Emirates. We study traditional SIR models in general and focus on a time-dependent SIR model, which has been proven more adaptive and robust in predicting the COVID-19 outbreak. We evaluate the time-dependent SIR model. Then, we implement a Monte Carlo model. The Monte Carlo model uses the parameters extracted from the Time-Dependent SIR Model. The Monte Carlo model exhibited a better prediction accuracy and resembles the data collected from the Ministry of Cabinet Affairs, United Arab Emirates, between April and July 2020

A Study on Predicting the Outbreak of COVID-19 in the United Arab Emirates: A Monte Carlo Simulation Approach

According to the World Health Organization updates, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a pandemic between 2019 and 2022, with millions of confirmed cases and deaths worldwide. There are various approaches to predicting the suspected, infected, and recovered (SIR) cases with different factual or epidemiological models. Some of the recent approaches to predicting the COVID-19 outbreak have had positive impacts in specific nations. Results show that the SIR model is a significant tool to cast the dynamics and predictions of the COVID-19 outbreak compared to other epidemic models. In this paper, we employ the Monte Carlo simulation to predict the spread of COVID-19 in the United Arab Emirates. We study traditional SIR models in general and focus on a time-dependent SIR model, which has been proven more adaptive and robust in predicting the COVID-19 outbreak. We evaluate the time-dependent SIR model. Then, we implement a Monte Carlo model. The Monte Carlo model uses the parameters extracted from the Time-Dependent SIR Model. The Monte Carlo model exhibited a better prediction accuracy and resembles the data collected from the Ministry of Cabinet Affairs, United Arab Emirates, between April and July 2020

Secure Plug-in Electric Vehicle (PEV) Charging in a Smart Grid Network

Charging of plug-in electric vehicles (PEVs) exposes smart grid systems and their users to different kinds of security and privacy attacks. Hence, a secure charging protocol is required for PEV charging. Existing PEV charging protocols are usually based on insufficiently represented and simplified charging models that do not consider the user’s charging modes (charging at a private location, charging as a guest user, roaming within one’s own supplier network or roaming within other suppliers’ networks). However, the requirement for charging protocols depends greatly on the user’s charging mode. Consequently, available solutions do not provide complete protocol specifications. Moreover, existing protocols do not support anonymous user authentication and payment simultaneously. In this paper, we propose a comprehensive end-to-end charging protocol that addresses the security and privacy issues in PEV charging. The proposed protocol uses nested signatures to protect users’ privacy from external suppliers, their own suppliers and third parties. Our approach supports anonymous user authentication, anonymous payment, as well as anonymous message exchange between suppliers within a hierarchical smart grid architecture. We have verified our protocol using the AVISPA software verification tool and the results showed that our protocol is secure and works as desired