Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures

Large-scale instruction data is essential for aligning pretrained Large Language Models (LLMs) with human instructions, but may contain sensitive information that hinders its public sharing. Federated Learning (FL) enables collaborative fine-tuning of LLMs without accessing raw data. However, existing approaches to federated LLM fine-tuning usually adopt a uniform model architecture, making it hard to fit highly heterogeneous client-side data in varying domains and formats. To address this, we propose FedAMoLE, a lightweight personalized FL framework that enables data-driven heterogeneous model architectures. This framework features a heterogeneous mixture of LoRA experts module for aggregating architecturally heterogeneous models and a reverse selection-based expert assignment strategy that optimizes model architectures based on data distributions. Experiments across five scenarios show that FedAMoLE improves client-side performance by an average of 5.14% compared to existing approaches while maintaining scalability.