Data Divergence-aware Client Selection via Knowledge Graph for Federated LLM Fine-tuning

With the rapid development of edge devices and growing awareness of privacy protection, recent developers have transformed to fine-tune LLMs via federated learning instead of centralized training. Federated fine-tuning can leverage distributed data sources and computation power, but it also suffers from system and statistical heterogeneity. Client selection is an effective tool to solve the system and statistical heterogeneity in FL, but existing client selection schemes that involve online measurement will not be as effective in LLM fine-tuning as in conventional FL due to the huge LLM size and fewer fine-tuning rounds. In this paper, to the best of our knowledge, we are the first to consider both system and statistical heterogeneity in federated LLM fine-tuning, and we formulate a new latency minimization problem. We propose to measure client data overlap via knowledge graph offline to assist client selection in federated LLM fine-tuning. Our client selection scheme excels in both model accuracy and fine-tuning latency. We evaluate our scheme via two LLMs and two applications via four datasets. The experiment results illustrate that our scheme achieves the highest accuracy while 2.05x faster than the baselines.