# EcoLoRA: Communication-Efficient Federated Fine-Tuning of Large Language Models

arXiv: https://arxiv.org/abs/2506.02001 · HTML: https://mlsanity.com/paper/2506.02001/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2025.

## Built on by (most cited first, 4 of 4)

- [FERA: Uncertainty-Aware Federated Reasoning for Large Language Models](https://mlsanity.com/paper/2605.10082/) (2026)
- [A Federated Fine-Tuning Paradigm of Foundation Models in Heterogenous Wireless Networks](https://mlsanity.com/paper/2509.19306/) (2025)
- [Thermo-FL: Thermal-Aware Robust Federated Fine-Tuning of Large Language Models for Edge AI](https://mlsanity.com/paper/2608.21172/) (2026)
- [FedFit: Federated Fine-Tuning of LLMs via Vector-Bank Parameterization and Quantization](https://mlsanity.com/paper/2610.01537/) (2026)

## Abstract (arXiv)

To address data locality and privacy restrictions, Federated Learning (FL) has recently been adopted to fine-tune large language models (LLMs), enabling improved performance on various downstream tasks without requiring aggregated data. However, the repeated exchange of model updates in FL can result in prohibitively high communication costs, hindering the distributed learning process. To address this challenge, we propose EcoLoRA, a novel communication-efficient federated fine-tuning framework for LLMs. Leveraging the modular structure, we propose a round-robin segment sharing scheme, where each client uploads only a complementary LoRA segment per round to reduce network bandwidth. It is further combined with adaptive sparsification methods tailored to LoRA's training dynamics and lossless encoding techniques. We conduct extensive evaluations on both question-answering and value-alignment tasks across multiple datasets and models. The results show that EcoLoRA significantly reduces communication overhead without compromising performance. For instance, it reduces communication time by up to 79% and total training time by up to 65%.