@inproceedings{liu-etal-2025-ecolora,
title = "{E}co{L}o{RA}: Communication-Efficient Federated Fine-Tuning of Large Language Models",
author = "Liu, Han and
Wen, Ruoyao and
Nair, Srijith and
Liu, Jia and
Lou, Wenjing and
Zhang, Chongjie and
Yeoh, William and
Vorobeychik, Yevgeniy and
Zhang, Ning",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1046/",
doi = "10.18653/v1/2025.emnlp-main.1046",
pages = "20732--20746",
ISBN = "979-8-89176-332-6",
abstract = "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{\%}."
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<abstract>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%.</abstract>
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%0 Conference Proceedings
%T EcoLoRA: Communication-Efficient Federated Fine-Tuning of Large Language Models
%A Liu, Han
%A Wen, Ruoyao
%A Nair, Srijith
%A Liu, Jia
%A Lou, Wenjing
%A Zhang, Chongjie
%A Yeoh, William
%A Vorobeychik, Yevgeniy
%A Zhang, Ning
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F liu-etal-2025-ecolora
%X 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%.
%R 10.18653/v1/2025.emnlp-main.1046
%U https://aclanthology.org/2025.emnlp-main.1046/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1046
%P 20732-20746
Markdown (Informal)
[EcoLoRA: Communication-Efficient Federated Fine-Tuning of Large Language Models](https://aclanthology.org/2025.emnlp-main.1046/) (Liu et al., EMNLP 2025)
ACL
- Han Liu, Ruoyao Wen, Srijith Nair, Jia Liu, Wenjing Lou, Chongjie Zhang, William Yeoh, Yevgeniy Vorobeychik, and Ning Zhang. 2025. EcoLoRA: Communication-Efficient Federated Fine-Tuning of Large Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 20732–20746, Suzhou, China. Association for Computational Linguistics.