We explore on-device self-supervised collaborative fine-tuning of large
language models with limited local data availability. Taking inspiration from
the collaborative learning community, we introduce three distinct
trust-weighted gradient aggregation schemes: weight similarity-based,
prediction similarity-based and validation performance-based. To minimize
communication overhead, we integrate Low-Rank Adaptation (LoRA) and only
exchange LoRA weight updates. Our protocols, driven by prediction and
performance metrics, surpass both FedAvg and local fine-tuning methods, which
is particularly evident in realistic scenarios with more diverse local data
distributions. The results underscore the effectiveness of our approach in
addressing heterogeneity and scarcity within local datasets.

在设备上进行自我监督的合作微调大规模语言模型的研究，使用三种不同的信任加权梯度集成方案，并与 FedAvg 和本地微调方法相比，使用少量的数据交换和 LoRA 权重更新，显示出在本地数据分布不均的现实场景中处理异质性和稀缺性方面的有效性。

个性化协作微调用于设备上的大型语言模型

Personalized Collaborative Fine-Tuning for On-Device Large Language  Models

The prevalent communication efficient federated learning (FL) frameworks
usually take advantages of model gradient compression or model distillation.
However, the unbalanced local data distributions (either in quantity or
quality) of participating clients, contributing non-equivalently to the global
model training, still pose a big challenge to these works. In this paper, we
propose FedCliP, a novel communication efficient FL framework that allows
faster model training, by adaptively learning which clients should remain
active for further model training and pruning those who should be inactive with
less potential contributions. We also introduce an alternative optimization
method with a newly defined contribution score measure to facilitate active and
inactive client determination. We empirically evaluate the communication
efficiency of FL frameworks with extensive experiments on three benchmark
datasets under both IID and non-IID settings. Numerical results demonstrate the
outperformance of the porposed FedCliP framework over state-of-the-art FL
frameworks, i.e., FedCliP can save 70% of communication overhead with only 0.2%
accuracy loss on MNIST datasets, and save 50% and 15% of communication
overheads with less than 1% accuracy loss on FMNIST and CIFAR-10 datasets,
respectively.

本研究提出了一种新的通信高效的联邦学习（FL）框架 FedCliP，采用自适应学习算法确定参与模型训练的客户端，通过优化算法减少不必要的通信开销。实验结果显示该框架在 MNIST、FMNIST 和 CIFAR-10 等数据集上优于目前的 FL 框架。