EcoLoRA: Communication-Efficient Federated Fine-Tuning of Large Language Models (2025.emnlp-main)
Copied to clipboard
Han Liu, Ruoyao Wen, Srijith Nair, Jia Liu, Wenjing Lou, Chongjie Zhang, William Yeoh, Yevgeniy Vorobeychik, Ning Zhang
| Challenge: | Recurrent exchange of model updates in FL can result in prohibitively high communication costs, hindering the distributed learning process. |
| Approach: | They propose a federated fine-tuning framework that uses a round-robin segment sharing scheme to reduce network bandwidth and adaptive sparsification methods tailored to LoRA’s training dynamics. |
| Outcome: | The proposed framework reduces communication overhead without compromising performance on question-answering and value-alignment tasks. |
Similar Papers
Federated LoRA Fine-Tuning with Pipelined Error-Mitigated Aggregation and Matrix-Wise Freezing (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for fine-tuning large language models often suffer from biased model aggregation and are hindered by significant communication and computation burden. |
| Approach: | They propose a Federated low-rank adaptation system for large language models that leverages pipelined error-mitigated model aggregation and adaptive matrix-wise parameter freezing to mitigate aggregations. |
| Outcome: | The proposed system improves time-to-target by 2.17-8.48 on real-world datasets. |
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) excel in translation and summarization due to the capabilities of transformer architectures. |
| Approach: | They propose to integrate tensorized adapters into model encoder/decoder blocks to improve model adaptability against data heterogeneity. |
| Outcome: | Experiments on large-scale cross-device FL and large-silo FL show that the proposed methods perform on par or even better than existing federated PEFT approaches while reducing communication cost. |
GMFL: Efficient Global Masking for Federated LLM Fine-tuning (2026.acl-long)
Copied to clipboard
| Challenge: | Low-Rank Adaptation (LoRA) has emerged as a prominent solution to mitigate the communication and computation costs in federated fine-tuning of Large Language Models (LLMs). |
| Approach: | They propose a plug-and-play layer freezing mechanism to integrate with existing federated fine-tuning frameworks. |
| Outcome: | The proposed solution reduces communication overhead and lowers computational costs while preserving the performance of the underlying federated fine-tuning methods. |
Federated Data-Efficient Instruction Tuning for Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing federated learning (FL) uses all local data, causing excessive computational overhead and overfitting to local data. |
| Approach: | They propose a federated data-efficient instruction tuning approach which utilizes a representative subset of edge-side data to tune LLMs. |
| Outcome: | The proposed method improves Rouge-L on unseen tasks by 10.72% over the SOTA full-data instruction tuning methods while using less than 1.5% of the data samples. |
FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for fine-tuning Large Language Models (LLMs) suffer from a performance bottleneck . Existing approaches like Offsite-Tuning (OT) secure the LLMs IP . |
| Approach: | They propose a framework that replaces weak adapters with a unified, powerful Proxy Small Language Model (SLM) they propose 'resource-friendly' compression and 'robust optimization' to handle data heterogeneity. |
| Outcome: | Experiments show that FedProxy outperforms OT and centralized fine-tuning methods. |
FanLoRA: Fantastic LoRAs and Where to Find Them in Large Language Model Fine-tuning (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Lowrank adaptation and its variants introduce significant latency in multi-tenant settings, hindering their applications in the industry. |
| Approach: | They propose a framework to fine-tune LoRA modules on a large-scale instruction tuning dataset. |
| Outcome: | The proposed framework outperforms existing PEFT methods and significantly reduces inference latency. |
Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing frameworks for learning Large Language Models (LLMs) require adaptive data processing and low-rank adjustment to improve accuracy and fine-tuning speed. |
| Approach: | They propose a fisher information-based adaptive federated curriculum learning framework with two novel methods to improve FL fine-tuning process. |
| Outcome: | The proposed framework improves performance and fine-tuning speed compared with baseline approaches. |
LeLoRA: Learnable Low-Rank Adaptation of Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to fine-tuning large language models (LLMs) rely on manually specified and fixed hyperparameters, resulting in suboptimal performance and low parameter efficiency. |
| Approach: | They propose a framework that allows for dynamically learned adaptive adaptation strategies to be used to fine-tune large language models. |
| Outcome: | The proposed framework outperforms baselines in adapting large language models. |
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA (2024.findings-emnlp)
Copied to clipboard
Zhu JianHao, Changze Lv, Xiaohua Wang, Muling Wu, Wenhao Liu, Tianlong Li, Zixuan Ling, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Existing federated learning frameworks require substantial data and computational resources to develop large language models. |
| Approach: | They propose a method that distributes a quantized version of the model’s parameters during training and combine it with a popular fine-tuning method to significantly reduce communication costs. |
| Outcome: | The proposed method enables accurate estimations for parameter updates while preventing clients from accessing a model whose performance is comparable to the centrally hosted one. |
FedDQC: Data Quality Control in Federated Instruction-tuning of Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Federated Learning (FL) enables privacy-preserving collaborative instruction tuning of large language models. |
| Approach: | They propose a federated instruction tuning framework with dynamic data quality control to solve this problem. |
| Outcome: | The proposed framework improves performance on mixed-quality datasets on synthetic and real-world datasets. |