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

Abstract: Large-scale instruction data is essential for aligning pretrained 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.