DeepAI AI Chat
Log In Sign Up

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

by   Aman Priyanshu, et al.

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.


page 1

page 2

page 3

page 4


Comparative assessment of federated and centralized machine learning

Federated Learning (FL) is a privacy preserving machine learning scheme,...

A Practical Cross-Device Federated Learning Framework over 5G Networks

The concept of federated learning (FL) was first proposed by Google in 2...

DPCOVID: Privacy-Preserving Federated Covid-19 Detection

Coronavirus (COVID-19) has shown an unprecedented global crisis by the d...

Unbounded Gradients in Federated Learning with Buffered Asynchronous Aggregation

Synchronous updates may compromise the efficiency of cross-device federa...

Federated Few-shot Learning for Cough Classification with Edge Devices

Automatically classifying cough sounds is one of the most critical tasks...

Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data

At this moment, databanks worldwide contain brain images of previously u...