arXiv (All)
open access
FedLAFP: Low-Rank Aggregation Meets Full-Rank Personalization in Federated Fine-Tuning
Federated parameter-efficient fine-tuning enables clients to adapt pre-trained models without sharing raw data or communicating the full model, but statistical heterogeneity makes a single global adapter insufficient for personalized prediction. Existing personalized methods typically use the same l
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning.#785547
- DR-LoRA: Dynamic Rank LoRA for Fine-Tuning Mixture-of-Experts Models#799932
- FedHUR: Learning Hierarchical Utility-Guided Client Relations for Personalized Federated Recommendation#999334
- SPRInG: Continual LLM Personalization via Selective Parametric Adaptation and Retrieval-Interpolated Generation#793550
- SPRInG: Continual LLM Personalization via Selective Parametric Adaptation and Retrieval-Interpolated Generation#789546
Record · ID 1132234
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive