arXiv AI
LLM-Based Schema-Aware Split Learning for Privacy-Preserving Mental Distress Prediction Across Heterogeneous Surveys
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FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data
arXiv:2605. 18936v2 Announce Type: replace Abstract: Social media text data are often used to train Machine Learning (ML) models to identify users exhibiting high-risk mental health behaviors.
arXiv AI
Sep 4
Privacy-Preserving Heterogeneous Multi-LLM Federated Inference for Cognitive Diagnosis
The paper introduces a federated inference framework that enables multiple commercial large language model (LLM) APIs—such as LLaMA‑3.3‑70B, GPT‑4o‑mini, and Claude‑3‑Haiku—to collaborate on cognitive diagnosis tasks without accessing raw student data or proprietary model internals. Each entity’s predictions are perturbed with Laplace noise to provide epsilon‑local differential privacy, and a residual‑based aggregation scheme mitigates model heterogeneity. Experiments on three educational benchmarks demonstrate strong privacy guarantees with minimal accuracy loss, confirming the framework’s practical usability and cross‑domain generalizability.
arXiv AI
Sep 10
LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback
arXiv:2605.30273v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show promise in generating supportive responses for mental health queries, but improving their usefulness, empat...
arXiv AI
Sep 2
Breaking the Structural Identity: Personalized Federated LoRA Fine-tuning under Rank Heterogeneity
The paper introduces FedRoRA, a federated learning framework that combines Low‑Rank Adaptation (LoRA) with rank‑heterogeneous personalization. It separates model adaptation into shared global directions and client‑specific rank‑wise magnitudes, using SVD on the server to extract a global subspace and a personalized projection with top‑k selection for each client. Experiments on natural language understanding and generation tasks show that FedRoRA outperforms existing state‑of‑the‑art methods.
arXiv AI
Jun 16
SDFLoRA: Selective Decoupled Federated LoRA for Privacy-preserving Fine-tuning with Heterogeneous Clients
arXiv:2601. 11219v3 Announce Type: replace-cross Abstract: Federated learning (FL) for large language models (LLMs) has attracted increasing attention as a privacy-preserving approach for adapting models over distributed data, where parameter-efficient methods such as Low-Rank Adaptation (LoRA) are widely adopted to reduce communication and memory costs.
arXiv Machine Learning
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FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
arXiv:2502. 17748v4 Announce Type: replace Abstract: Federated Learning (FL) inherently mitigates mass data centralization risks; however, its privacy protections are not equally distributed - leaving vulnerable individuals disproportionately exposed to sophisticated privacy attacks.