Enhancing sports image data classification in federated learning through genetic algorithm-based optimization of base architecture Article Swipe
Desheng Fu
,
Jie Huang
,
D. K. Hazra
,
Amit Kumar Dwivedi
,
Suneet Kumar Gupta
,
Basu Dev Shivahare
,
Deepak Garg
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0303462
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0303462
Nowadays, federated learning is one of the most prominent choices for making decisions. A significant benefit of federated learning is that, unlike deep learning, it is not necessary to share data samples with the model owner. The weight of the global model in traditional federated learning is created by averaging the weights of all clients or sites. In the proposed work, a novel method has been discussed to generate an optimized base model without hampering its performance, which is based on a genetic algorithm. Chromosome representation, crossover, and mutation—all the intermediate operations of the genetic algorithm have been illustrated with useful examples. After applying the genetic algorithm, there is a significant improvement in inference time and a huge reduction in storage space. Therefore, the model can be easily deployed on resource-constrained devices. For the experimental work, sports data has been used in balanced and unbalanced scenarios with various numbers of clients in a federated learning environment. In addition, we have used four famous deep learning architectures, such as AlexNet, VGG19, ResNet50, and EfficientNetB3, as the base model. We have achieved 92.34% accuracy with 9 clients in the balanced data set by using EfficientNetB3 as the base model using a GA-based approach. Moreover, after applying the genetic algorithm to optimize EfficientNetB3, there is an improvement in inference time and storage space by 20% and 2.35%, respectively.
Related Topics
Concepts
Computer science
Genetic algorithm
Crossover
Inference
Base (topology)
Artificial intelligence
Machine learning
Deep learning
Set (abstract data type)
Representation (politics)
Data mining
Reduction (mathematics)
Chromosome
Algorithm
Mathematics
Biochemistry
Law
Mathematical analysis
Gene
Programming language
Politics
Geometry
Political science
Chemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0303462
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0303462&type=printable
- OA Status
- gold
- Cited By
- 7
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400526768
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400526768Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0303462Digital Object Identifier
- Title
-
Enhancing sports image data classification in federated learning through genetic algorithm-based optimization of base architectureWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-11Full publication date if available
- Authors
-
Desheng Fu, Jie Huang, D. K. Hazra, Amit Kumar Dwivedi, Suneet Kumar Gupta, Basu Dev Shivahare, Deepak GargList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pone.0303462Publisher landing page
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0303462&type=printableDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0303462&type=printableDirect OA link when available
- Concepts
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Computer science, Genetic algorithm, Crossover, Inference, Base (topology), Artificial intelligence, Machine learning, Deep learning, Set (abstract data type), Representation (politics), Data mining, Reduction (mathematics), Chromosome, Algorithm, Mathematics, Biochemistry, Law, Mathematical analysis, Gene, Programming language, Politics, Geometry, Political science, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
58Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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