FedMed-LoRA: a federated parameter-efficient fine-tuning framework with dual lora for medical multi-organ segmentation

Multi-organ segmentation is critical for clinical diagnosis and treatment planning, yet real-world deployment is hindered by cross-center imaging heterogeneity, scarce annotations, limited computational resources, and strict privacy constraints. Vision foundation models such as SAM provide strong representations but are impractical for on-site fine-tuning due to their scale and communication cost. We propose FedMed-LoRA, a federated and parameter-efficient fine-tuning framework for adapting foundation models to medical imaging. FedMed-LoRA operates on a fully frozen SAM encoder and introduces dual low-rank adaptations in both attention and MLP pathways, enabling effective medical-domain transfer with only a small set of trainable parameters. INT4-quantized frozen weights combined with BF16 low-rank training substantially reduce memory requirements, allowing large models to be fine-tuned on hospital-level hardware. Within a federated workflow, only low-rank updates are shared, preserving data privacy and model intellectual property. Experiments demonstrate that FedMed-LoRA matches full-parameter fine-tuning on FLARE22 and achieves superior cross-domain performance on Synapse, outperforming MedSAM, Med-SA, and SAMed. Ablations confirm the complementary roles of the two LoRA paths. FedMed-LoRA offers an efficient, privacy-preserving, and clinically deployable solution for medical multi-organ segmentation.