Recrutement
Lynx HR OCR - Federated Learning Client
Privacy-first federated learning across Lynx HR tenants — shares anonymized skill adjacencies + multilingual section labels + resume-layout fingerprints. Default OFF.
lynx_hr_ocr_federated
· v19.0.1.0.0
· Premium
What this solves
Lynx HR OCR - Federated Learning Client
Privacy-first federated learning across opt-in Lynx HR tenants - share anonymized skill adjacencies, layout fingerprints, and section labels; get back curated improvements that make every tenant's resume parsing better. Default OFF.
The biggest weakness of any single-tenant resume parser is that it only sees one company's data. Federated learning lets every opt-in tenant contribute aggregated signals (never raw text, never PII, gated by K-anonymity) and pull down curated improvements. The result is a parser that improves for everyone without anyone exposing their candidates.
Key Features
Anonymized skill adjacencies - shares which skills appear together on confirmed-hire resumes (after K=5 anonymity gate).
Layout fingerprints - resume-layout dHashes shared so the parser recognises common templates faster.
Multilingual section labels - "Experience" in twelve languages aggregated across tenants.
Skill canonicalization aliases - "JS" / "Javascript" / "ECMAScript" merging learned from real-world data.
Strict exclusions - resume text, names, emails, phones, addresses, photos, school names, candidate-specific values, salaries, and bias results are NEVER shared.
Default OFF - explicit opt-in required; per-tenant kill switch and air-gap mode supported.
K-anonymity floor - any pattern requires five distinct tenants before it ships.
Integrates With
lynx_hr_ocr - the resume parser this hub strengthens.
lynx_hr_recruitment_score - contributes the score-relevant features.
The central Lynx HR hub server (managed by Patrii Cloud).
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