Clean‐label poisoning attacks on federated learning for IoT
Article Swipe
Jie Yang
,
Jun Zheng
,
Thar Baker
,
Shuai Tang
,
Yu‐an Tan
,
Quanxin Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1111/exsy.13161
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1111/exsy.13161
Federated Learning (FL) is suitable for the application scenarios of distributed edge collaboration of the Internet of Things (IoT). It can provide data security and privacy, which is why it is widely used in the IoT applications such as Industrial IoT (IIoT). Latest research shows that the federated learning framework is vulnerable to poisoning attacks in the case of an active attack by the adversary. However, the existing backdoor attack methods are easy to be detected by the defence methods. To address this challenge, we focus on edge‐cloud synergistic FL clean‐label attacks. Unlike common backdoor attack, to ensure the attack's concealment, we add a small perturbation to realize the clean label attack by judging the cosine similarity between the gradient of the adversarial loss and the gradient of the normal training loss. In order to improve the attack success rate and robustness, the attack is implemented when the global model is about to converge. The experimental results verified that 1% of poisoned data could make an attack successful with a high probability. Our method maintains stealth while performing model poisoning attacks, and the average Peak Signal‐to‐Noise Ratio (PSNR) of poisoning images reaches over 30 dB, and the average Structural SIMilarity (SSIM) is close to 0.93. Most importantly, our attack method can bypass the Byzantine aggregation defence.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/exsy.13161
- OA Status
- green
- Cited By
- 15
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4304782753
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4304782753Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1111/exsy.13161Digital Object Identifier
- Title
-
Clean‐label poisoning attacks on federated learning for
IoT Work title - Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-12Full publication date if available
- Authors
-
Jie Yang, Jun Zheng, Thar Baker, Shuai Tang, Yu‐an Tan, Quanxin ZhangList of authors in order
- Landing page
-
https://doi.org/10.1111/exsy.13161Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://research.brighton.ac.uk/en/publications/e2a5b6bd-9953-41a5-950f-8fb7aa2d14aeDirect OA link when available
- Concepts
-
Computer science, Backdoor, Computer security, Attack model, Robustness (evolution), Adversary, Internet of Things, Cloud computing, Chemistry, Biochemistry, Gene, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 7, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4304782753 |
|---|---|
| doi | https://doi.org/10.1111/exsy.13161 |
| ids.doi | https://doi.org/10.1111/exsy.13161 |
| ids.openalex | https://openalex.org/W4304782753 |
| fwci | 2.9369823 |
| type | article |
| title | Clean‐label poisoning attacks on federated learning for |
| awards[0].id | https://openalex.org/G8446706986 |
| awards[0].funder_id | https://openalex.org/F4320335777 |
| awards[0].display_name | |
| awards[0].funder_award_id | 2020YFB1712101 |
| awards[0].funder_display_name | National Key Research and Development Program of China |
| awards[1].id | https://openalex.org/G6454313764 |
| awards[1].funder_id | https://openalex.org/F4320321001 |
| awards[1].display_name | |
| awards[1].funder_award_id | 62072037 |
| awards[1].funder_display_name | National Natural Science Foundation of China |
| awards[2].id | https://openalex.org/G3814875541 |
| awards[2].funder_id | https://openalex.org/F4320321001 |
| awards[2].display_name | |
| awards[2].funder_award_id | U1936218 |
| awards[2].funder_display_name | National Natural Science Foundation of China |
| biblio.issue | 5 |
| biblio.volume | 40 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10764 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9998000264167786 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Privacy-Preserving Technologies in Data |
| topics[1].id | https://openalex.org/T11689 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9980999827384949 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Adversarial Robustness in Machine Learning |
| topics[2].id | https://openalex.org/T11775 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9843000173568726 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2741 |
| topics[2].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[2].display_name | COVID-19 diagnosis using AI |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| funders[1].id | https://openalex.org/F4320335777 |
| funders[1].ror | |
| funders[1].display_name | National Key Research and Development Program of China |
| is_xpac | False |
| apc_list.value | 3860 |
| apc_list.currency | USD |
| apc_list.value_usd | 3860 |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8847960829734802 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C2781045450 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7220613956451416 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q254569 |
| concepts[1].display_name | Backdoor |
| concepts[2].id | https://openalex.org/C38652104 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5866113901138306 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[2].display_name | Computer security |
| concepts[3].id | https://openalex.org/C65856478 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5593264698982239 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q3991682 |
| concepts[3].display_name | Attack model |
| concepts[4].id | https://openalex.org/C63479239 |
| concepts[4].level | 3 |
| concepts[4].score | 0.5568865537643433 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q7353546 |
| concepts[4].display_name | Robustness (evolution) |
| concepts[5].id | https://openalex.org/C41065033 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5370236039161682 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2825412 |
| concepts[5].display_name | Adversary |
| concepts[6].id | https://openalex.org/C81860439 |
| concepts[6].level | 2 |
| concepts[6].score | 0.47984763979911804 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q251212 |
| concepts[6].display_name | Internet of Things |
| concepts[7].id | https://openalex.org/C79974875 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4143421947956085 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q483639 |
| concepts[7].display_name | Cloud computing |
| concepts[8].id | https://openalex.org/C185592680 |
| concepts[8].level | 0 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[8].display_name | Chemistry |
| concepts[9].id | https://openalex.org/C55493867 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[9].display_name | Biochemistry |
| concepts[10].id | https://openalex.org/C104317684 |
| concepts[10].level | 2 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[10].display_name | Gene |
| concepts[11].id | https://openalex.org/C111919701 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[11].display_name | Operating system |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8847960829734802 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/backdoor |
| keywords[1].score | 0.7220613956451416 |
| keywords[1].display_name | Backdoor |
| keywords[2].id | https://openalex.org/keywords/computer-security |
| keywords[2].score | 0.5866113901138306 |
| keywords[2].display_name | Computer security |
| keywords[3].id | https://openalex.org/keywords/attack-model |
| keywords[3].score | 0.5593264698982239 |
| keywords[3].display_name | Attack model |
| keywords[4].id | https://openalex.org/keywords/robustness |
| keywords[4].score | 0.5568865537643433 |
| keywords[4].display_name | Robustness (evolution) |
| keywords[5].id | https://openalex.org/keywords/adversary |
| keywords[5].score | 0.5370236039161682 |
| keywords[5].display_name | Adversary |
| keywords[6].id | https://openalex.org/keywords/internet-of-things |
| keywords[6].score | 0.47984763979911804 |
| keywords[6].display_name | Internet of Things |
| keywords[7].id | https://openalex.org/keywords/cloud-computing |
| keywords[7].score | 0.4143421947956085 |
| keywords[7].display_name | Cloud computing |
| language | en |
| locations[0].id | doi:10.1111/exsy.13161 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S72232612 |
| locations[0].source.issn | 0266-4720, 1468-0394 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0266-4720 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Expert Systems |
| locations[0].source.host_organization | https://openalex.org/P4310320595 |
| locations[0].source.host_organization_name | Wiley |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320595 |
| locations[0].source.host_organization_lineage_names | Wiley |
| locations[0].license | public-domain |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/public-domain |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Expert Systems |
| locations[0].landing_page_url | https://doi.org/10.1111/exsy.13161 |
| locations[1].id | pmh:oai:pure.atira.dk:openaire/e2a5b6bd-9953-41a5-950f-8fb7aa2d14ae |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400216 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | Research Portal (King's College London) |
| locations[1].source.host_organization | https://openalex.org/I183935753 |
| locations[1].source.host_organization_name | King's College London |
| locations[1].source.host_organization_lineage | https://openalex.org/I183935753 |
| locations[1].license | other-oa |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/other-oa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Yang, J, Zheng, J, Baker, T, Tang, S, Tan, Y A & Zhang, Q 2022, 'Clean-label poisoning attacks on federated learning for IoT', Expert Systems. https://doi.org/10.1111/exsy.13161 |
| locations[1].landing_page_url | https://research.brighton.ac.uk/en/publications/e2a5b6bd-9953-41a5-950f-8fb7aa2d14ae |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5060493055 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-7574-4512 |
| authorships[0].author.display_name | Jie Yang |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I125839683 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China |
| authorships[0].institutions[0].id | https://openalex.org/I125839683 |
| authorships[0].institutions[0].ror | https://ror.org/01skt4w74 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I125839683, https://openalex.org/I890469752 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Beijing Institute of Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jie Yang |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China |
| authorships[1].author.id | https://openalex.org/A5108060154 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-3596-6246 |
| authorships[1].author.display_name | Jun Zheng |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I125839683 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China |
| authorships[1].institutions[0].id | https://openalex.org/I125839683 |
| authorships[1].institutions[0].ror | https://ror.org/01skt4w74 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I125839683, https://openalex.org/I890469752 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Beijing Institute of Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Jun Zheng |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China |
| authorships[2].author.id | https://openalex.org/A5065487579 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-5166-4873 |
| authorships[2].author.display_name | Thar Baker |
| authorships[2].countries | AE |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I29891158 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah, UAE |
| authorships[2].institutions[0].id | https://openalex.org/I29891158 |
| authorships[2].institutions[0].ror | https://ror.org/00engpz63 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I29891158 |
| authorships[2].institutions[0].country_code | AE |
| authorships[2].institutions[0].display_name | University of Sharjah |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Thar Baker |
| authorships[2].is_corresponding | True |
| authorships[2].raw_affiliation_strings | Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah, UAE |
| authorships[3].author.id | https://openalex.org/A5073448968 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-8467-0022 |
| authorships[3].author.display_name | Shuai Tang |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I125839683 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China |
| authorships[3].institutions[0].id | https://openalex.org/I125839683 |
| authorships[3].institutions[0].ror | https://ror.org/01skt4w74 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I125839683, https://openalex.org/I890469752 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Beijing Institute of Technology |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Shuai Tang |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China |
| authorships[4].author.id | https://openalex.org/A5003198271 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-6404-8853 |
| authorships[4].author.display_name | Yu‐an Tan |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I125839683 |
| authorships[4].affiliations[0].raw_affiliation_string | School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China |
| authorships[4].institutions[0].id | https://openalex.org/I125839683 |
| authorships[4].institutions[0].ror | https://ror.org/01skt4w74 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I125839683, https://openalex.org/I890469752 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Beijing Institute of Technology |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Yu‐an Tan |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China |
| authorships[5].author.id | https://openalex.org/A5017675981 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-5094-7388 |
| authorships[5].author.display_name | Quanxin Zhang |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I125839683 |
| authorships[5].affiliations[0].raw_affiliation_string | School of Computer Science, Beijing Institute of Technology, Beijing, China |
| authorships[5].institutions[0].id | https://openalex.org/I125839683 |
| authorships[5].institutions[0].ror | https://ror.org/01skt4w74 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I125839683, https://openalex.org/I890469752 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Beijing Institute of Technology |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Quanxin Zhang |
| authorships[5].is_corresponding | True |
| authorships[5].raw_affiliation_strings | School of Computer Science, Beijing Institute of Technology, Beijing, China |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://research.brighton.ac.uk/en/publications/e2a5b6bd-9953-41a5-950f-8fb7aa2d14ae |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Clean‐label poisoning attacks on federated learning for |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10764 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9998000264167786 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Privacy-Preserving Technologies in Data |
| related_works | https://openalex.org/W4366850823, https://openalex.org/W3086120435, https://openalex.org/W4328053081, https://openalex.org/W2889233174, https://openalex.org/W3012113073, https://openalex.org/W3143283098, https://openalex.org/W4283753393, https://openalex.org/W3204619801, https://openalex.org/W4214812658, https://openalex.org/W4387796593 |
| cited_by_count | 15 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 6 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 7 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 2 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:pure.atira.dk:openaire/e2a5b6bd-9953-41a5-950f-8fb7aa2d14ae |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400216 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Research Portal (King's College London) |
| best_oa_location.source.host_organization | https://openalex.org/I183935753 |
| best_oa_location.source.host_organization_name | King's College London |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I183935753 |
| best_oa_location.license | other-oa |
| best_oa_location.pdf_url | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | article |
| best_oa_location.license_id | https://openalex.org/licenses/other-oa |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | Yang, J, Zheng, J, Baker, T, Tang, S, Tan, Y A & Zhang, Q 2022, 'Clean-label poisoning attacks on federated learning for IoT', Expert Systems. https://doi.org/10.1111/exsy.13161 |
| best_oa_location.landing_page_url | https://research.brighton.ac.uk/en/publications/e2a5b6bd-9953-41a5-950f-8fb7aa2d14ae |
| primary_location.id | doi:10.1111/exsy.13161 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S72232612 |
| primary_location.source.issn | 0266-4720, 1468-0394 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0266-4720 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Expert Systems |
| primary_location.source.host_organization | https://openalex.org/P4310320595 |
| primary_location.source.host_organization_name | Wiley |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320595 |
| primary_location.source.host_organization_lineage_names | Wiley |
| primary_location.license | public-domain |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/public-domain |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Expert Systems |
| primary_location.landing_page_url | https://doi.org/10.1111/exsy.13161 |
| publication_date | 2022-10-12 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W3020864676, https://openalex.org/W2768475350, https://openalex.org/W6752600739, https://openalex.org/W2903356604, https://openalex.org/W3014775455, https://openalex.org/W2990614164, https://openalex.org/W3083109707, https://openalex.org/W2535838896, https://openalex.org/W3187356235, https://openalex.org/W2943329843, https://openalex.org/W6745621080, https://openalex.org/W3029501138, https://openalex.org/W3122605355, https://openalex.org/W2796004214, https://openalex.org/W2416799949, https://openalex.org/W3024699729, https://openalex.org/W3043061209, https://openalex.org/W4281763929, https://openalex.org/W2930249865, https://openalex.org/W2912213068, https://openalex.org/W4224250975, https://openalex.org/W3084847664, https://openalex.org/W3198064724, https://openalex.org/W3173613770, https://openalex.org/W4287671850, https://openalex.org/W4298140072, https://openalex.org/W2810065831, https://openalex.org/W3213515577, https://openalex.org/W4297687186, https://openalex.org/W4294597994, https://openalex.org/W3087391814, https://openalex.org/W3048715803 |
| referenced_works_count | 32 |
| abstract_inverted_index.a | 104, 170 |
| abstract_inverted_index.1% | 160 |
| abstract_inverted_index.30 | 194 |
| abstract_inverted_index.FL | 90 |
| abstract_inverted_index.In | 133 |
| abstract_inverted_index.It | 20 |
| abstract_inverted_index.To | 81 |
| abstract_inverted_index.an | 60, 166 |
| abstract_inverted_index.as | 39 |
| abstract_inverted_index.be | 75 |
| abstract_inverted_index.by | 63, 77, 113 |
| abstract_inverted_index.in | 34, 56 |
| abstract_inverted_index.is | 4, 28, 31, 51, 145, 151, 202 |
| abstract_inverted_index.it | 30 |
| abstract_inverted_index.of | 10, 14, 17, 59, 121, 128, 161, 189 |
| abstract_inverted_index.on | 87 |
| abstract_inverted_index.to | 53, 74, 97, 107, 135, 153, 204 |
| abstract_inverted_index.we | 85, 102 |
| abstract_inverted_index.IoT | 36, 41 |
| abstract_inverted_index.Our | 173 |
| abstract_inverted_index.The | 155 |
| abstract_inverted_index.add | 103 |
| abstract_inverted_index.and | 25, 125, 141, 182, 196 |
| abstract_inverted_index.are | 72 |
| abstract_inverted_index.can | 21, 211 |
| abstract_inverted_index.dB, | 195 |
| abstract_inverted_index.for | 6 |
| abstract_inverted_index.our | 208 |
| abstract_inverted_index.the | 7, 15, 35, 47, 57, 64, 67, 78, 99, 109, 115, 119, 122, 126, 129, 137, 143, 148, 183, 197, 213 |
| abstract_inverted_index.why | 29 |
| abstract_inverted_index.(FL) | 3 |
| abstract_inverted_index.Most | 206 |
| abstract_inverted_index.Peak | 185 |
| abstract_inverted_index.case | 58 |
| abstract_inverted_index.data | 23, 163 |
| abstract_inverted_index.easy | 73 |
| abstract_inverted_index.edge | 12 |
| abstract_inverted_index.high | 171 |
| abstract_inverted_index.loss | 124 |
| abstract_inverted_index.make | 165 |
| abstract_inverted_index.over | 193 |
| abstract_inverted_index.rate | 140 |
| abstract_inverted_index.such | 38 |
| abstract_inverted_index.that | 46, 159 |
| abstract_inverted_index.this | 83 |
| abstract_inverted_index.used | 33 |
| abstract_inverted_index.when | 147 |
| abstract_inverted_index.with | 169 |
| abstract_inverted_index.0.93. | 205 |
| abstract_inverted_index.Ratio | 187 |
| abstract_inverted_index.about | 152 |
| abstract_inverted_index.clean | 110 |
| abstract_inverted_index.close | 203 |
| abstract_inverted_index.could | 164 |
| abstract_inverted_index.focus | 86 |
| abstract_inverted_index.label | 111 |
| abstract_inverted_index.loss. | 132 |
| abstract_inverted_index.model | 150, 179 |
| abstract_inverted_index.order | 134 |
| abstract_inverted_index.shows | 45 |
| abstract_inverted_index.small | 105 |
| abstract_inverted_index.which | 27 |
| abstract_inverted_index.while | 177 |
| abstract_inverted_index.(IoT). | 19 |
| abstract_inverted_index.(PSNR) | 188 |
| abstract_inverted_index.(SSIM) | 201 |
| abstract_inverted_index.Latest | 43 |
| abstract_inverted_index.Things | 18 |
| abstract_inverted_index.Unlike | 93 |
| abstract_inverted_index.active | 61 |
| abstract_inverted_index.attack | 62, 70, 112, 138, 144, 167, 209 |
| abstract_inverted_index.bypass | 212 |
| abstract_inverted_index.common | 94 |
| abstract_inverted_index.cosine | 116 |
| abstract_inverted_index.ensure | 98 |
| abstract_inverted_index.global | 149 |
| abstract_inverted_index.images | 191 |
| abstract_inverted_index.method | 174, 210 |
| abstract_inverted_index.normal | 130 |
| abstract_inverted_index.widely | 32 |
| abstract_inverted_index.(IIoT). | 42 |
| abstract_inverted_index.address | 82 |
| abstract_inverted_index.attack, | 96 |
| abstract_inverted_index.attacks | 55 |
| abstract_inverted_index.average | 184, 198 |
| abstract_inverted_index.between | 118 |
| abstract_inverted_index.defence | 79 |
| abstract_inverted_index.improve | 136 |
| abstract_inverted_index.judging | 114 |
| abstract_inverted_index.methods | 71 |
| abstract_inverted_index.provide | 22 |
| abstract_inverted_index.reaches | 192 |
| abstract_inverted_index.realize | 108 |
| abstract_inverted_index.results | 157 |
| abstract_inverted_index.stealth | 176 |
| abstract_inverted_index.success | 139 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 66 |
| abstract_inverted_index.Internet | 16 |
| abstract_inverted_index.Learning | 2 |
| abstract_inverted_index.attack's | 100 |
| abstract_inverted_index.attacks, | 181 |
| abstract_inverted_index.attacks. | 92 |
| abstract_inverted_index.backdoor | 69, 95 |
| abstract_inverted_index.defence. | 216 |
| abstract_inverted_index.detected | 76 |
| abstract_inverted_index.existing | 68 |
| abstract_inverted_index.gradient | 120, 127 |
| abstract_inverted_index.learning | 49 |
| abstract_inverted_index.methods. | 80 |
| abstract_inverted_index.poisoned | 162 |
| abstract_inverted_index.privacy, | 26 |
| abstract_inverted_index.research | 44 |
| abstract_inverted_index.security | 24 |
| abstract_inverted_index.suitable | 5 |
| abstract_inverted_index.training | 131 |
| abstract_inverted_index.verified | 158 |
| abstract_inverted_index.Byzantine | 214 |
| abstract_inverted_index.Federated | 1 |
| abstract_inverted_index.converge. | 154 |
| abstract_inverted_index.federated | 48 |
| abstract_inverted_index.framework | 50 |
| abstract_inverted_index.maintains | 175 |
| abstract_inverted_index.poisoning | 54, 180, 190 |
| abstract_inverted_index.scenarios | 9 |
| abstract_inverted_index.Industrial | 40 |
| abstract_inverted_index.SIMilarity | 200 |
| abstract_inverted_index.Structural | 199 |
| abstract_inverted_index.adversary. | 65 |
| abstract_inverted_index.challenge, | 84 |
| abstract_inverted_index.performing | 178 |
| abstract_inverted_index.similarity | 117 |
| abstract_inverted_index.successful | 168 |
| abstract_inverted_index.vulnerable | 52 |
| abstract_inverted_index.adversarial | 123 |
| abstract_inverted_index.aggregation | 215 |
| abstract_inverted_index.application | 8 |
| abstract_inverted_index.distributed | 11 |
| abstract_inverted_index.implemented | 146 |
| abstract_inverted_index.robustness, | 142 |
| abstract_inverted_index.synergistic | 89 |
| abstract_inverted_index.applications | 37 |
| abstract_inverted_index.concealment, | 101 |
| abstract_inverted_index.edge‐cloud | 88 |
| abstract_inverted_index.experimental | 156 |
| abstract_inverted_index.importantly, | 207 |
| abstract_inverted_index.perturbation | 106 |
| abstract_inverted_index.probability. | 172 |
| abstract_inverted_index.clean‐label | 91 |
| abstract_inverted_index.collaboration | 13 |
| abstract_inverted_index.Signal‐to‐Noise | 186 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5065487579, https://openalex.org/A5017675981 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I125839683, https://openalex.org/I29891158 |
| citation_normalized_percentile.value | 0.89400862 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |