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

arXiv: https://arxiv.org/abs/2104.01864 · HTML: https://mlsanity.com/paper/2104.01864/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 5 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)

## Built on by (most cited first, 3 of 3)

- [Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning](https://mlsanity.com/paper/2110.01643/) (2021)
- [When Differential Privacy Meets Interpretability: A Case Study](https://mlsanity.com/paper/2106.13203/) (2021)
- [A Federated Approach for Hate Speech Detection](https://mlsanity.com/paper/2302.09243/) (2023)

## Abstract (arXiv)

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.