Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approach Article Swipe
Monik Raj Behera
,
Sudhir K. Upadhyay
,
Suresh Shetty
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2107.10243
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2107.10243
Over the recent years, Federated machine learning continues to gain interest and momentum where there is a need to draw insights from data while preserving the data provider's privacy. However, one among other existing challenges in the adoption of federated learning has been the lack of fair, transparent and universally agreed incentivization schemes for rewarding the federated learning contributors. Smart contracts on a blockchain network provide transparent, immutable and independently verifiable proofs by all participants of the network. We leverage this open and transparent nature of smart contracts on a blockchain to define incentivization rules for the contributors, which is based on a novel scalar quantity - federated contribution. Such a smart contract based reward-driven model has the potential to revolutionize the federated learning adoption in enterprises. Our contribution is two-fold: first is to show how smart contract based blockchain can be a very natural communication channel for federated learning. Second, leveraging this infrastructure, we can show how an intuitive measure of each agents' contribution can be built and integrated with the life cycle of the training and reward process.
Related Topics
Concepts
Smart contract
Computer science
Leverage (statistics)
Verifiable secret sharing
Blockchain
Federated learning
Mathematical proof
Process (computing)
Computer security
Data science
Knowledge management
Artificial intelligence
Geometry
Mathematics
Programming language
Operating system
Set (abstract data type)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2107.10243
- https://arxiv.org/pdf/2107.10243
- OA Status
- green
- Cited By
- 1
- References
- 32
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3184992308
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3184992308Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2107.10243Digital Object Identifier
- Title
-
Federated Learning using Smart Contracts on Blockchains, based on Reward Driven ApproachWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-07-19Full publication date if available
- Authors
-
Monik Raj Behera, Sudhir K. Upadhyay, Suresh ShettyList of authors in order
- Landing page
-
https://arxiv.org/abs/2107.10243Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2107.10243Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2107.10243Direct OA link when available
- Concepts
-
Smart contract, Computer science, Leverage (statistics), Verifiable secret sharing, Blockchain, Federated learning, Mathematical proof, Process (computing), Computer security, Data science, Knowledge management, Artificial intelligence, Geometry, Mathematics, Programming language, Operating system, Set (abstract data type)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3184992308 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2107.10243 |
| ids.doi | https://doi.org/10.48550/arxiv.2107.10243 |
| ids.mag | 3184992308 |
| ids.openalex | https://openalex.org/W3184992308 |
| fwci | |
| type | preprint |
| title | Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approach |
| biblio.issue | |
| biblio.volume | |
| 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/T10270 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9994000196456909 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1710 |
| topics[1].subfield.display_name | Information Systems |
| topics[1].display_name | Blockchain Technology Applications and Security |
| topics[2].id | https://openalex.org/T10237 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9814000129699707 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Cryptography and Data Security |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2779950589 |
| concepts[0].level | 3 |
| concepts[0].score | 0.7169069647789001 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q7544035 |
| concepts[0].display_name | Smart contract |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6494507193565369 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C153083717 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6140893697738647 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q6535263 |
| concepts[2].display_name | Leverage (statistics) |
| concepts[3].id | https://openalex.org/C85847156 |
| concepts[3].level | 3 |
| concepts[3].score | 0.5607356429100037 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q59015987 |
| concepts[3].display_name | Verifiable secret sharing |
| concepts[4].id | https://openalex.org/C2779687700 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5486279129981995 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q20514253 |
| concepts[4].display_name | Blockchain |
| concepts[5].id | https://openalex.org/C2992525071 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5457527041435242 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q50818671 |
| concepts[5].display_name | Federated learning |
| concepts[6].id | https://openalex.org/C108710211 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5178137421607971 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11538 |
| concepts[6].display_name | Mathematical proof |
| concepts[7].id | https://openalex.org/C98045186 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4248560965061188 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q205663 |
| concepts[7].display_name | Process (computing) |
| concepts[8].id | https://openalex.org/C38652104 |
| concepts[8].level | 1 |
| concepts[8].score | 0.38543814420700073 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[8].display_name | Computer security |
| concepts[9].id | https://openalex.org/C2522767166 |
| concepts[9].level | 1 |
| concepts[9].score | 0.35651078820228577 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2374463 |
| concepts[9].display_name | Data science |
| concepts[10].id | https://openalex.org/C56739046 |
| concepts[10].level | 1 |
| concepts[10].score | 0.33103570342063904 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q192060 |
| concepts[10].display_name | Knowledge management |
| concepts[11].id | https://openalex.org/C154945302 |
| concepts[11].level | 1 |
| concepts[11].score | 0.30587488412857056 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[11].display_name | Artificial intelligence |
| concepts[12].id | https://openalex.org/C2524010 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[12].display_name | Geometry |
| concepts[13].id | https://openalex.org/C33923547 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[13].display_name | Mathematics |
| concepts[14].id | https://openalex.org/C199360897 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[14].display_name | Programming language |
| concepts[15].id | https://openalex.org/C111919701 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[15].display_name | Operating system |
| concepts[16].id | https://openalex.org/C177264268 |
| concepts[16].level | 2 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[16].display_name | Set (abstract data type) |
| keywords[0].id | https://openalex.org/keywords/smart-contract |
| keywords[0].score | 0.7169069647789001 |
| keywords[0].display_name | Smart contract |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6494507193565369 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/leverage |
| keywords[2].score | 0.6140893697738647 |
| keywords[2].display_name | Leverage (statistics) |
| keywords[3].id | https://openalex.org/keywords/verifiable-secret-sharing |
| keywords[3].score | 0.5607356429100037 |
| keywords[3].display_name | Verifiable secret sharing |
| keywords[4].id | https://openalex.org/keywords/blockchain |
| keywords[4].score | 0.5486279129981995 |
| keywords[4].display_name | Blockchain |
| keywords[5].id | https://openalex.org/keywords/federated-learning |
| keywords[5].score | 0.5457527041435242 |
| keywords[5].display_name | Federated learning |
| keywords[6].id | https://openalex.org/keywords/mathematical-proof |
| keywords[6].score | 0.5178137421607971 |
| keywords[6].display_name | Mathematical proof |
| keywords[7].id | https://openalex.org/keywords/process |
| keywords[7].score | 0.4248560965061188 |
| keywords[7].display_name | Process (computing) |
| keywords[8].id | https://openalex.org/keywords/computer-security |
| keywords[8].score | 0.38543814420700073 |
| keywords[8].display_name | Computer security |
| keywords[9].id | https://openalex.org/keywords/data-science |
| keywords[9].score | 0.35651078820228577 |
| keywords[9].display_name | Data science |
| keywords[10].id | https://openalex.org/keywords/knowledge-management |
| keywords[10].score | 0.33103570342063904 |
| keywords[10].display_name | Knowledge management |
| keywords[11].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[11].score | 0.30587488412857056 |
| keywords[11].display_name | Artificial intelligence |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2107.10243 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2107.10243 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2107.10243 |
| locations[1].id | mag:3184992308 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | arXiv (Cornell University) |
| locations[1].landing_page_url | http://export.arxiv.org/pdf/2107.10243 |
| locations[2].id | doi:10.48550/arxiv.2107.10243 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400194 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | arXiv (Cornell University) |
| locations[2].source.host_organization | https://openalex.org/I205783295 |
| locations[2].source.host_organization_name | Cornell University |
| locations[2].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | |
| locations[2].raw_type | article |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | https://doi.org/10.48550/arxiv.2107.10243 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5069062692 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-9385-2533 |
| authorships[0].author.display_name | Monik Raj Behera |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Monik Raj Behera |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5008095998 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-2228-8063 |
| authorships[1].author.display_name | Sudhir K. Upadhyay |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Sudhir Upadhyay |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5110113435 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Suresh Shetty |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Suresh Shetty |
| authorships[2].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2107.10243 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approach |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| 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/W3182280856, https://openalex.org/W2921970644, https://openalex.org/W2885936017, https://openalex.org/W2958789237, https://openalex.org/W2948805388, https://openalex.org/W2783790728, https://openalex.org/W2998558987, https://openalex.org/W2995439221, https://openalex.org/W3082368518, https://openalex.org/W2948602552, https://openalex.org/W3129828944, https://openalex.org/W3180084572, https://openalex.org/W2962771602, https://openalex.org/W2514202461, https://openalex.org/W3207184590, https://openalex.org/W2604122668, https://openalex.org/W3111742335, https://openalex.org/W2946378091, https://openalex.org/W3023634038, https://openalex.org/W2985634786 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:arXiv.org:2107.10243 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| 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 | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2107.10243 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2107.10243 |
| primary_location.id | pmh:oai:arXiv.org:2107.10243 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2107.10243 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2107.10243 |
| publication_date | 2021-07-19 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W3014517104, https://openalex.org/W1522301498, https://openalex.org/W2797481382, https://openalex.org/W3043762149, https://openalex.org/W2970196897, https://openalex.org/W3090984959, https://openalex.org/W1988584731, https://openalex.org/W2540262818, https://openalex.org/W2810807736, https://openalex.org/W3034764457, https://openalex.org/W2955213239, https://openalex.org/W2913570153, https://openalex.org/W2127048411, https://openalex.org/W2963828549, https://openalex.org/W2800197349, https://openalex.org/W2990700079, https://openalex.org/W2091432990, https://openalex.org/W2011112377, https://openalex.org/W3006921589, https://openalex.org/W2987317136, https://openalex.org/W2914212774, https://openalex.org/W2557090183, https://openalex.org/W2897371887, https://openalex.org/W2171683886, https://openalex.org/W2281564060, https://openalex.org/W2168446235, https://openalex.org/W2535838896, https://openalex.org/W2750384547, https://openalex.org/W3151748982, https://openalex.org/W2989253922, https://openalex.org/W3015292646, https://openalex.org/W2914328083 |
| referenced_works_count | 32 |
| abstract_inverted_index.- | 106 |
| abstract_inverted_index.a | 16, 62, 89, 102, 110, 142 |
| abstract_inverted_index.We | 78 |
| abstract_inverted_index.an | 158 |
| abstract_inverted_index.be | 141, 166 |
| abstract_inverted_index.by | 72 |
| abstract_inverted_index.in | 35, 125 |
| abstract_inverted_index.is | 15, 99, 129, 132 |
| abstract_inverted_index.of | 38, 45, 75, 85, 161, 174 |
| abstract_inverted_index.on | 61, 88, 101 |
| abstract_inverted_index.to | 8, 18, 91, 119, 133 |
| abstract_inverted_index.we | 154 |
| abstract_inverted_index.Our | 127 |
| abstract_inverted_index.all | 73 |
| abstract_inverted_index.and | 11, 48, 68, 82, 168, 177 |
| abstract_inverted_index.can | 140, 155, 165 |
| abstract_inverted_index.for | 53, 95, 147 |
| abstract_inverted_index.has | 41, 116 |
| abstract_inverted_index.how | 135, 157 |
| abstract_inverted_index.one | 30 |
| abstract_inverted_index.the | 1, 25, 36, 43, 55, 76, 96, 117, 121, 171, 175 |
| abstract_inverted_index.Over | 0 |
| abstract_inverted_index.Such | 109 |
| abstract_inverted_index.been | 42 |
| abstract_inverted_index.data | 22, 26 |
| abstract_inverted_index.draw | 19 |
| abstract_inverted_index.each | 162 |
| abstract_inverted_index.from | 21 |
| abstract_inverted_index.gain | 9 |
| abstract_inverted_index.lack | 44 |
| abstract_inverted_index.life | 172 |
| abstract_inverted_index.need | 17 |
| abstract_inverted_index.open | 81 |
| abstract_inverted_index.show | 134, 156 |
| abstract_inverted_index.this | 80, 152 |
| abstract_inverted_index.very | 143 |
| abstract_inverted_index.with | 170 |
| abstract_inverted_index.Smart | 59 |
| abstract_inverted_index.among | 31 |
| abstract_inverted_index.based | 100, 113, 138 |
| abstract_inverted_index.built | 167 |
| abstract_inverted_index.cycle | 173 |
| abstract_inverted_index.fair, | 46 |
| abstract_inverted_index.first | 131 |
| abstract_inverted_index.model | 115 |
| abstract_inverted_index.novel | 103 |
| abstract_inverted_index.other | 32 |
| abstract_inverted_index.rules | 94 |
| abstract_inverted_index.smart | 86, 111, 136 |
| abstract_inverted_index.there | 14 |
| abstract_inverted_index.where | 13 |
| abstract_inverted_index.which | 98 |
| abstract_inverted_index.while | 23 |
| abstract_inverted_index.agreed | 50 |
| abstract_inverted_index.define | 92 |
| abstract_inverted_index.nature | 84 |
| abstract_inverted_index.proofs | 71 |
| abstract_inverted_index.recent | 2 |
| abstract_inverted_index.reward | 178 |
| abstract_inverted_index.scalar | 104 |
| abstract_inverted_index.years, | 3 |
| abstract_inverted_index.Second, | 150 |
| abstract_inverted_index.agents' | 163 |
| abstract_inverted_index.channel | 146 |
| abstract_inverted_index.machine | 5 |
| abstract_inverted_index.measure | 160 |
| abstract_inverted_index.natural | 144 |
| abstract_inverted_index.network | 64 |
| abstract_inverted_index.provide | 65 |
| abstract_inverted_index.schemes | 52 |
| abstract_inverted_index.However, | 29 |
| abstract_inverted_index.adoption | 37, 124 |
| abstract_inverted_index.contract | 112, 137 |
| abstract_inverted_index.existing | 33 |
| abstract_inverted_index.insights | 20 |
| abstract_inverted_index.interest | 10 |
| abstract_inverted_index.learning | 6, 40, 57, 123 |
| abstract_inverted_index.leverage | 79 |
| abstract_inverted_index.momentum | 12 |
| abstract_inverted_index.network. | 77 |
| abstract_inverted_index.privacy. | 28 |
| abstract_inverted_index.process. | 179 |
| abstract_inverted_index.quantity | 105 |
| abstract_inverted_index.training | 176 |
| abstract_inverted_index.Federated | 4 |
| abstract_inverted_index.continues | 7 |
| abstract_inverted_index.contracts | 60, 87 |
| abstract_inverted_index.federated | 39, 56, 107, 122, 148 |
| abstract_inverted_index.immutable | 67 |
| abstract_inverted_index.intuitive | 159 |
| abstract_inverted_index.learning. | 149 |
| abstract_inverted_index.potential | 118 |
| abstract_inverted_index.rewarding | 54 |
| abstract_inverted_index.two-fold: | 130 |
| abstract_inverted_index.blockchain | 63, 90, 139 |
| abstract_inverted_index.challenges | 34 |
| abstract_inverted_index.integrated | 169 |
| abstract_inverted_index.leveraging | 151 |
| abstract_inverted_index.preserving | 24 |
| abstract_inverted_index.provider's | 27 |
| abstract_inverted_index.verifiable | 70 |
| abstract_inverted_index.transparent | 47, 83 |
| abstract_inverted_index.universally | 49 |
| abstract_inverted_index.contribution | 128, 164 |
| abstract_inverted_index.enterprises. | 126 |
| abstract_inverted_index.participants | 74 |
| abstract_inverted_index.transparent, | 66 |
| abstract_inverted_index.communication | 145 |
| abstract_inverted_index.contribution. | 108 |
| abstract_inverted_index.contributors, | 97 |
| abstract_inverted_index.contributors. | 58 |
| abstract_inverted_index.independently | 69 |
| abstract_inverted_index.revolutionize | 120 |
| abstract_inverted_index.reward-driven | 114 |
| abstract_inverted_index.incentivization | 51, 93 |
| abstract_inverted_index.infrastructure, | 153 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |