mlsanityData to 7 Oct 2026

Personalized Collaborative Fine-Tuning for On-Device Large Language Models

arXiv 2404.09753 ↗First posted 15 Apr 2024cs.CL cs.LGcited by 9

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Abstract (arXiv)

We explore on-device self-supervised collaborative fine-tuning of large language models with limited local data availability. Taking inspiration from the collaborative learning community, we introduce three distinct trust-weighted gradient aggregation schemes: weight similarity-based, prediction similarity-based and validation performance-based. To minimize communication overhead, we integrate Low-Rank Adaptation (LoRA) and only exchange LoRA weight updates. Our protocols, driven by prediction and performance metrics, surpass both FedAvg and local fine-tuning methods, which is particularly evident in realistic scenarios with more diverse local data distributions. The results underscore the effectiveness of our approach in addressing heterogeneity and scarcity within local datasets.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 14 of its 20 references that are in the map. First posted on arXiv 15 Apr 2024.

Builds on

14 of 20 references.

References · 14

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  4. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  5. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
  6. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  7. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  8. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  9. 2023Towards Building the Federated GPT: Federated Instruction Tuningcited by 184
  10. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 121
  11. 2023Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimizationcited by 65
  12. 2023Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Datacited by 26
2 more
  1. 2022Find Your Friends: Personalized Federated Learning with the Right Collaboratorscited by 10
  2. 2023Collaborative Learning via Prediction Consensuscited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 4; 2026: 1.

9 papers.

  1. 2025A Survey of Personalized Large Language Models: Progress and Future Directionscited by 65
  2. 2024Low-Rank Adaptation for Foundation Models: A Comprehensive Reviewcited by 35
  3. 2024On-Device Collaborative Language Modeling via a Mixture of Generalists and Specialistscited by 6
  4. 2024CoBo: Collaborative Learning via Bilevel Optimizationcited by 5
  5. 2025Learning Steerable Clarification Policies with Collaborative Self-playcited by 5
  6. 2025DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Modelscited by 2
  7. 2024Revisiting Federated Fine-Tuning: A Single Communication Round is Enough for Foundation Modelscited by 1
  8. 2025Lorica: A Synergistic Fine-Tuning Framework for Advancing Personalized Adversarial Robustnesscited by 0
  9. 2026PubSwap: Public-Data Off-Policy Coordination for Federated RLVRcited by 0