Towards On-Device Personalization: Cloud-device Collaborative Data Augmentation for Efficient On-device Language Model

2025-08-28

AI Summary

Current large language models (LLMs) suffer from insufficient personalization and heavy reliance on cloud infrastructure. To address these limitations, we propose a cloud-edge collaborative personalization framework: the cloud leverages LLMs to generate high-quality synthetic data and performs parameter-efficient fine-tuning (PEFT), while the edge integrates real and synthetic data for lightweight personalized training and standalone inference. This approach is the first to systematically synergize cloud-side generalization capability with edge-side personalization demands, alleviating user data sparsity through collaborative data augmentation. Evaluated across six downstream tasks, our method significantly improves personalization performance while eliminating network dependency—ensuring low-latency response and preserving local data privacy. Our core contribution is the establishment of the first cloud-edge joint fine-tuning paradigm that simultaneously achieves personalization, computational efficiency, and privacy preservation.

Technology Category

Machine Learning: Learning on the Edge & Model CompressionNatural Language Processing: (Large) Language ModelsComputer Vision: Large Vision Models

Application Category

User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSearch and Retrieval-Augmented AI: Personalized, context-aware and across-device searchSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

With the advancement of large language models (LLMs), significant progress has been achieved in various Natural Language Processing (NLP) tasks. However, existing LLMs still face two major challenges that hinder their broader adoption: (1) their responses tend to be generic and lack personalization tailored to individual users, and (2) they rely heavily on cloud infrastructure due to intensive computational requirements, leading to stable network dependency and response delay. Recent research has predominantly focused on either developing cloud-based personalized LLMs or exploring the on-device deployment of general-purpose LLMs. However, few studies have addressed both limitations simultaneously by investigating personalized on-device language models. To bridge this gap, we propose CDCDA-PLM, a framework for deploying personalized on-device language models on user devices with support from a powerful cloud-based LLM. Specifically, CDCDA-PLM leverages the server-side LLM's strong generalization capabilities to augment users' limited personal data, mitigating the issue of data scarcity. Using both real and synthetic data, A personalized on-device language models (LMs) is fine-tuned via parameter-efficient fine-tuning (PEFT) modules and deployed on users' local devices, enabling them to process queries without depending on cloud-based LLMs. This approach eliminates reliance on network stability and ensures high response speeds. Experimental results across six tasks in a widely used personalization benchmark demonstrate the effectiveness of CDCDA-PLM.

Problem

• Enabling personalized on-device language models for individual users
• Overcoming data scarcity through cloud-device collaborative augmentation
• Eliminating cloud dependency for network-independent high-speed responses

Innovation

• Cloud-device collaborative data augmentation
• Parameter-efficient fine-tuning for personalization
• On-device deployment eliminating cloud dependency