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LLM-based split learning enables privacy-preserving mental health data analysis

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel schema-aware split learning framework designed to enable privacy-preserving analysis of mental health survey data across different institutions. This approach utilizes a large language model, specifically LLaMA-3.2-3B-Instruct, as a shared semantic encoder to harmonize disparate survey schemas. The framework partitions the LLM, allowing clients to retain raw data locally while offloading intensive computation to a server, thereby minimizing client-side processing and ensuring data privacy. The proposed method demonstrates strong performance, achieving an average ANLS of 0.708 with limited training data and outperforming federated learning in most settings. AI

IMPACT This research could enable more effective collaborative analysis of sensitive mental health data across institutions, potentially leading to better predictive models and interventions.

RANK_REASON Research paper detailing a novel methodology for privacy-preserving data analysis using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM-based split learning enables privacy-preserving mental health data analysis

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Research paper detailing a novel methodology for privacy-preserving data analysis using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Md Khalid Syfullah, Alvi Ataur Khalil ·

    LLM-Based Schema-Aware Split Learning for Privacy-Preserving Mental Distress Prediction Across Heterogeneous Surveys

    arXiv:2609.15871v1 Announce Type: cross Abstract: Rising societal and lifestyle complexity has been linked to a growing prevalence of mental distress worldwide. Educational institutions, workplaces, clinics, etc. collect large volumes of mental health survey data to understand an…

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