Year: 2024
Type: article
Abstract: The COVID-19 pandemic has highlighted the importance of artificial intelligence (AI) and data-driven solutions in enabling rapid response and decision-making support. To contain the pandemic, medical professionals must collaborate with experts in other fields, such as data scientists, to analyze medical data, including electronic health records (EHR), and to find solutions as fast as possible. However, within a sensitive industry such as healthcare, significant challenges related to data access persist due to privacy concerns and regulations. To solve these challenges, federated learning (FL) provides a novel approach to pandemic response by enabling the use of decentralized private data while maintaining data privacy. This paper provides an overview of FL and explores its application in healthcare during the COVID-19 pandemic. It also introduces FLPanGuard, a conceptual framework that focuses on detecting and fighting a pandemic using FL and other technologies to enhance data collection and security. This framework provides a global response to pandemics starting in the pre-pandemic phase by predicting potential outbreaks using social media content and EHR. It is also expanding into the pandemic phase by assisting medical professionals in diagnosis and treatment. Finally, we explore the limitations of FL to highlight areas where further research is required.
Authors: Ibtissam Chouja, Sahar Saoud, Mohamed Sadik, Asma Sbai
Institutions: Université Ibn Zohr, Cadi Ayyad University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $312
OpenAlex ID: W4405578423