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。