Fed-LoRA: Cross-Domain Generative Knowledge Feedback for Few-Shot Medical Anomaly Detection

Abstract

Deep learning in medical imaging is severely hindered by the "domain gap," where significant imaging discrepancies exist even among images of the same modality and anatomical structure due to heterogeneous scanning devices and diverse clinical environments. While major medical centers possess high-fidelity data, resource-constrained clinics often struggle with legacy hardware and severe data scarcity. This imbalance leads to a significant collapse in diagnostic performance and poor model generalization when deployed in low-resource settings. To address these challenges, we propose a novel few-shot Federated Low-Rank Adaptation (Fed-LoRA) framework for cross-domain generative feedback. By leveraging the lightweight nature of LoRA, our method enables the efficient transmission of global pathological priors from a high-resource Hub to decentralized Edge nodes with minimal communication overhead. Crucially, this mechanism facilitates cross-domain data generation while ensuring privacy by precluding the transmission of raw medical data. This allows local models to synthesize high-fidelity abnormal exemplars precisely tailored to specific institutional styles. Extensive experiments demonstrate that our synthetic images significantly enhance the performance of downstream anomaly detection tasks. This work provides a scalable and secure solution for mitigating data scarcity and achieving effective cross-domain adaptation in decentralized healthcare systems.