split-learning

topic3 events

papersTODAY 04:00 UTC

LLM-based split learning predicts mental distress across heterogeneous surveys

A new arXiv paper proposes a schema-aware split learning approach that uses LLMs to predict mental distress from survey data while keeping sensitive records private. The method is designed to work across surveys with differing structures and questions, which is a common obstacle when pooling mental health data from schools, employers, and clinics. The work targets privacy-preserving collaboration, so data stays local rather than being centralized.

privacy/split-learning/mental-health/llm

papersTODAY 04:00 UTC

Question-Guided Token Pruning Proposed as Privacy Defense for Vision-Language Models

A new arXiv paper proposes pruning visual tokens based on the question being asked, rather than sending all visual features to the server in split-learning setups. The approach targets vision-language question answering in federated, split, and U-shaped split learning, where raw data stays local but transmitted representations can still leak information. The authors frame selective transmission as a way to reduce both privacy exposure and bandwidth use.

privacy/vision-language-models/federated-learning/efficiency

papersSEP 10 04:00 UTC

arXiv paper proposes privacy-preserving split learning for federated LLM fine-tuning

A new arXiv paper addresses fine-tuning large language models in settings where participants cannot host the full model locally, often because the model owner keeps it proprietary. The authors introduce a split learning approach designed to preserve privacy during federated fine-tuning on domain-specific data.

llm/privacy/federated-learning/research