FedPandemic: A Cross-Device Federated Learning Approach Towards Elementary Prognosis of Diseases During a Pandemic
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 3 of its 17 references that are in the map. First posted on arXiv 5 Apr 2021.
Builds on
3 of 17 references.
References · 3
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2018Deep contextualized word representationscited by 2,285
- 2019Towards Federated Learning at Scale: System Designcited by 826
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1.
3 papers.