Edge-FIT: Federated Instruction Tuning of Quantized LLMs for Privacy-Preserving Smart Home Environments

Vinay Venkatesh, Vamsidhar R Kamanuru, Lav Kumar, Nikita Kothari
9/28/2025
cs.LGcs.AI

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Abstract

This paper proposes Edge-FIT (Federated Instruction Tuning on the Edge), a scalable framework for Federated Instruction Tuning (FIT) of Large Language Models (LLMs). Traditional Federated Learning (TFL) methods, like FedAvg, fail when confronted with the massive parameter size of LLMs [3], [6]. Our Edge-FIT framework combines federated learning with 4-bit Quantized Low-Rank Adaptation (QLORA), mitigating the core issues of communication and computational overhead. We demonstrate this by filtering the general-purpose Databricks Dolly 15k dataset for the IoT domain. Experimental results show the Edge-FIT tuned Llama 2(7B) achieves an F1-Score of 0.89. We also demonstrate a viable trade-off using the 3.8B Phi-3-mini model, validating Edge-FIT as a scalable framework for decentralized LLM deployment on home compute gateways.

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Cite this paper

@article{venkatesh2025edgefit,
  title  = {Edge-FIT: Federated Instruction Tuning of Quantized LLMs for Privacy-Preserving Smart Home Environments},
  author = {Vinay Venkatesh and Vamsidhar R Kamanuru and Lav Kumar and Nikita Kothari},
  year   = {2025},
  eprint = {2510.03284},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.03284v1}
}

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