mlsanityData to 7 Oct 2026

A Comparative Study of Federated Learning Models for COVID-19 Detection

arXiv 2303.16141 ↗First posted 28 Mar 2023cs.LG cs.CV eess.IVcited by 3

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Abstract (arXiv)

Deep learning is effective in diagnosing COVID-19 and requires a large amount of data to be effectively trained. Due to data and privacy regulations, hospitals generally have no access to data from other hospitals. Federated learning (FL) has been used to solve this problem, where it utilizes a distributed setting to train models in hospitals in a privacy-preserving manner. Deploying FL is not always feasible as it requires high computation and network communication resources. This paper evaluates five FL algorithms' performance and resource efficiency for Covid-19 detection. A decentralized setting with CNN networks is set up, and the performance of FL algorithms is compared with a centralized environment. We examined the algorithms with varying numbers of participants, federated rounds, and selection algorithms. Our results show that cyclic weight transfer can have better overall performance, and results are better with fewer participating hospitals. Our results demonstrate good performance for detecting COVID-19 patients and might be useful in deploying FL algorithms for covid-19 detection and medical image analysis in general.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 28 references that are in the map. First posted on arXiv 28 Mar 2023.

Builds on

11 of 28 references.

References · 11

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  7. 2021On Large-Cohort Training for Federated Learningcited by 73
  8. 2020Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imagingcited by 56
  9. 2018Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Datacited by 46
  10. 2020Dynamic Fusion based Federated Learning for COVID-19 Detectioncited by 36
  11. 2020The challenges of deploying artificial intelligence models in a rapidly evolving pandemiccited by 12

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Cited by year

Citing papers per year: 2023: 3.

3 papers.

  1. 2023Exploring adversarial attacks in federated learning for medical imagingcited by 8
  2. 2023The Hidden Adversarial Vulnerabilities of Medical Federated Learningcited by 1
  3. 2023Tackling Heterogeneity in Medical Federated learning via Vision Transformerscited by 0