Federated Instruction Tuning (FIT) has shown the ability to achieve
collaborative model instruction tuning among massive data owners without
sharing private data. However, it still faces two key challenges, i.e., data
and resource heterogeneity. Due to the varying data distribution and
preferences among data owners, FIT cannot adapt to the personalized data of
individual owners. Moreover, clients with superior computational abilities are
constrained since they need to maintain the same fine-tuning architecture as
the weaker clients. To address these issues, we propose a novel Personalized
Federated Instruction Tuning (PerFIT) framework based on architecture search.
Specifically, PerFIT allows each client to search for a personalized
architecture by expanding the trainable parameter space of the global model
followed by pruning the parameters to the original state. This procedure allows
personalized instruction fine-tuning within expanded parameter spaces,
concurrently preserving the same number of trainable parameters. Furthermore,
to release the abilities of heterogeneous computational resources and enhance
the performance of personalization on local data, we exploit personalized
parameter-wise aggregation. The evaluation with multiple LLMs non-IID scenarios
demonstrates that compared to the state-of-the-art FIT methods, our approach
can achieve up to a 23% decrease in perplexity.

基于架构搜索的个性化联邦指导微调框架允许每个客户端在扩展的参数空间中搜索个性化架构，通过剪枝参数恢复到原始状态，以同时保留可训练参数的数量，在非完全独立的场景中，相比于最先进的 FIT 方法，我们的方法可以实现达到 23% 的困惑度降低。