Publication2024
Server-Enabled Information Transmission Through Networks Using Federated Learning Approach
The record
- Type
- Publication
- Date
- 1 January 2024
- Level
- International — held abroad
- Journal
- Lecture Notes in Networks and Systems
- Volume
- 1085 LNNS
- Pages
- 121-132
- Published
- 1 January 2024
- DOI
- 10.1007/978-981-97-6726-7_9
- Scopus ID
- 85209221069
- ISSN
- 23673370
- Authors
- 5
- Open access
- No
- Citations
- 2
- International co-authorship
- Yes
- Co-author countries
- GB, IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Federated learning research with differential privacy techniques represents innovation in machine learning algorithms for privacy-preserving distributed intelligence.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
16.4 Significantly reduce illicit financial and arms flows and combat all forms of organized crime
Privacy-preserving machine learning methodology reduces data leakage risks and cyberattack vulnerabilities in sensitive institutional and healthcare data transmission.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
17.6 Enhance North-South, South-South and triangular cooperation on science, technology and innovation, and knowledge sharing
Structural signal: co-authorship outside India.
- Rule-derived
- Emitted by a deterministic rule from structural signals rather than judged from the record’s text. The rule carries a version, so rule-derived and judged labels stay separable.
- Produced by
- sdg17-structural-v1, under kortex-publication-v1
Viksit Bharat @2047
vb-3.2 Digital technology and artificial intelligence
Digital technology innovation in federated learning and privacy enhancement directly advances artificial intelligence and computational capacity.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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Provenance
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Server-Enabled Information Transmission Through Networks Using Federated Learning Approach”. Kortex.