  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.
