  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.
