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The amount of data, manpower and capital required to understand, evaluate andagree on a group of symptoms for the elementary prognosis of pandemic diseasesis enormous. In this paper, we present FedPandemic, a novel noiseimplementation algorithm integrated with cross-device Federated learning forElementary symptom prognosis during a pandemic, taking COVID-19 as a casestudy. Our results display consistency and enhance robustness in recovering thecommon symptoms displayed by the disease, paving a faster and cheaper pathtowards symptom retrieval while also preserving the privacy of patient'ssymptoms via Federated learning.

FedPandemic: A Cross-Device Federated Learning Approach Towards Elementary Prognosis of Diseases During a Pandemic