Federated Learning and its Applications for Security and Communication Article Swipe
Hafiz M. Asif
,
Mohamed Abdul Karim
,
Firdous Kausar
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2022.0130838
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2022.0130838
The not so long ago, Artificial Intelligence (AI) has revolutionized our life by giving rise to the idea of self-learning in different environments. Amongst its different variants, Federated Learning (FL) is a novel approach that relies on decentralized communication data and its associated training. While reducing the amount of data acquired from users, federated learning derives the benefits of popular machine learning techniques, it brings learning to the edge or directly on-device. FL, frequently referred to as a new dawn in AI, is still in its early stages and is yet to garner widespread acceptance, owing to its (unknown) security and privacy implications. In this paper, we give an illustrative explanation of FL techniques, communication, and applications with privacy as well as security issues. According to our findings, there are fewer privacy-specific dangers linked with FL than security threats. We conclude the paper with the challenges of FL with special emphases on security.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14569/ijacsa.2022.0130838
- http://thesai.org/Downloads/Volume13No8/Paper_38-Federated_Learning_and_Its_Applications.pdf
- OA Status
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- Cited By
- 2
- References
- 23
- Related Works
- 10
- OpenAlex ID
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https://doi.org/10.14569/ijacsa.2022.0130838Digital Object Identifier
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Federated Learning and its Applications for Security and CommunicationWork title
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articleOpenAlex work type
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enPrimary language
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2022Year of publication
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2022-01-01Full publication date if available
- Authors
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Hafiz M. Asif, Mohamed Abdul Karim, Firdous KausarList of authors in order
- Landing page
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https://doi.org/10.14569/ijacsa.2022.0130838Publisher landing page
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https://thesai.org/Downloads/Volume13No8/Paper_38-Federated_Learning_and_Its_Applications.pdfDirect link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://thesai.org/Downloads/Volume13No8/Paper_38-Federated_Learning_and_Its_Applications.pdfDirect OA link when available
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Computer science, Computer security, Federated learning, Artificial intelligence, Enhanced Data Rates for GSM Evolution, Internet privacyTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.self-learning | 19 |
| abstract_inverted_index.communication, | 114 |
| abstract_inverted_index.revolutionized | 9 |
| abstract_inverted_index.privacy-specific | 131 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.62642127 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |