FedPandemic: A Cross-Device Federated Learning Approach Towards Elementary Prognosis of Diseases During a Pandemic
This work addresses the need for rapid and private symptom analysis during pandemics, offering a domain-specific incremental improvement over existing methods.
The paper tackled the problem of efficiently and privately identifying common symptoms of pandemic diseases like COVID-19 by proposing FedPandemic, a cross-device federated learning approach with a novel noise implementation algorithm, resulting in consistent and robust symptom retrieval that is faster and cheaper while preserving patient privacy.
The amount of data, manpower and capital required to understand, evaluate and agree on a group of symptoms for the elementary prognosis of pandemic diseases is enormous. In this paper, we present FedPandemic, a novel noise implementation algorithm integrated with cross-device Federated learning for Elementary symptom prognosis during a pandemic, taking COVID-19 as a case study. Our results display consistency and enhance robustness in recovering the common symptoms displayed by the disease, paving a faster and cheaper path towards symptom retrieval while also preserving the privacy of patient's symptoms via Federated learning.