# Personalized Federated Instruction Tuning via Neural Architecture Search

arXiv: https://arxiv.org/abs/2402.16919 · HTML: https://mlsanity.com/paper/2402.16919/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 24 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 18 of 18)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [Instruction Tuning with GPT-4](https://mlsanity.com/paper/2304.03277/) (2023)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Fine-Tuning Language Models with Just Forward Passes](https://mlsanity.com/paper/2305.17333/) (2023)
- [Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery](https://mlsanity.com/paper/2302.03668/) (2023)
- [Neural Architecture Search without Training](https://mlsanity.com/paper/2006.04647/) (2020)
- [Personalized Federated Learning using Hypernetworks](https://mlsanity.com/paper/2103.04628/) (2021)
- [Towards Building the Federated GPT: Federated Instruction Tuning](https://mlsanity.com/paper/2305.05644/) (2023)
- [Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2111.14213/) (2021)
- [Recovering Private Text in Federated Learning of Language Models](https://mlsanity.com/paper/2205.08514/) (2022)
- [Achieving Personalized Federated Learning with Sparse Local Models](https://mlsanity.com/paper/2201.11380/) (2022)
- [Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models](https://mlsanity.com/paper/2305.16597/) (2023)
- [Federated Neural Architecture Search](https://mlsanity.com/paper/2002.06352/) (2020)

## Built on by (most cited first, 3 of 3)

- [Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting](https://mlsanity.com/paper/2405.14252/) (2024)
- [Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures](https://mlsanity.com/paper/2411.19128/) (2024)
- [Federated Low-Rank Adaptation for Foundation Models: A Survey](https://mlsanity.com/paper/2505.13502/) (2025)

## Abstract (arXiv)

Federated Instruction Tuning (FIT) has shown the ability to achieve collaborative model instruction tuning among massive data owners without sharing private data. However, it still faces two key challenges, i.e., data and resource heterogeneity. Due to the varying data distribution and preferences among data owners, FIT cannot adapt to the personalized data of individual owners. Moreover, clients with superior computational abilities are constrained since they need to maintain the same fine-tuning architecture as the weaker clients. To address these issues, we propose a novel Personalized Federated Instruction Tuning (PerFIT) framework based on architecture search. Specifically, PerFIT allows each client to search for a personalized architecture by expanding the trainable parameter space of the global model followed by pruning the parameters to the original state. This procedure allows personalized instruction fine-tuning within expanded parameter spaces, concurrently preserving the same number of trainable parameters. Furthermore, to release the abilities of heterogeneous computational resources and enhance the performance of personalization on local data, we exploit personalized parameter-wise aggregation. The evaluation with multiple LLMs non-IID scenarios demonstrates that compared to the state-of-the-art FIT methods, our approach can achieve up to a 23% decrease in perplexity.