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Med-DualLoRA: Local Adaptation of Foundation Models for 3D Cardiac MRI

Joan Perramon-Llussà, Amelia Jiménez-Sánchez, Grzegorz Skorupko, Fotis Avgoustidis, Carlos Martín-Isla, Karim Lekadir, Polyxeni Gkontra

TL;DR

This approach provides a scalable solution for local federated adaptation of medical FMs under realistic clinical constraints and proposes Med-DualLoRA, a client-aware parameter-efficient fine-tuning (PEFT) federated framework that disentangles globally shared and local low-rank adaptations (LoRA) through additive decomposition.

Abstract

Foundation models (FMs) show great promise for robust downstream performance across medical imaging tasks and modalities, including cardiac magnetic resonance (CMR), following task-specific adaptation. However, adaptation using single-site data may lead to suboptimal performance and increased model bias, while centralized fine-tuning on clinical data is often infeasible due to privacy constraints. Federated fine-tuning offers a privacy-preserving alternative; yet conventional approaches struggle under heterogeneous, non-IID multi-center data and incur substantial communication overhead when adapting large models. In this work, we study federated FM fine-tuning for 3D CMR disease detection and propose Med-DualLoRA, a client-aware parameter-efficient fine-tuning (PEFT) federated framework that disentangles globally shared and local low-rank adaptations (LoRA) through additive decomposition. Global and local LoRA modules are trained locally, but only the global component is shared and aggregated across sites, keeping local adapters private. This design improves personalization while significantly reducing communication cost, and experiments show that adapting only two transformer blocks preserves performance while further improving efficiency. We evaluate our method on a multi-center state-of-the-art cine 3D CMR FM fine-tuned for disease detection using ACDC and combined M\&Ms datasets, treating each vendor as a federated client. Med-DualLoRA achieves statistically significant improved performance (balanced accuracy 0.768, specificity 0.612) compared to other federated PEFT baselines, while maintaining communication efficiency. Our approach provides a scalable solution for local federated adaptation of medical FMs under realistic clinical constraints.

Med-DualLoRA: Local Adaptation of Foundation Models for 3D Cardiac MRI

TL;DR

This approach provides a scalable solution for local federated adaptation of medical FMs under realistic clinical constraints and proposes Med-DualLoRA, a client-aware parameter-efficient fine-tuning (PEFT) federated framework that disentangles globally shared and local low-rank adaptations (LoRA) through additive decomposition.

Abstract

Foundation models (FMs) show great promise for robust downstream performance across medical imaging tasks and modalities, including cardiac magnetic resonance (CMR), following task-specific adaptation. However, adaptation using single-site data may lead to suboptimal performance and increased model bias, while centralized fine-tuning on clinical data is often infeasible due to privacy constraints. Federated fine-tuning offers a privacy-preserving alternative; yet conventional approaches struggle under heterogeneous, non-IID multi-center data and incur substantial communication overhead when adapting large models. In this work, we study federated FM fine-tuning for 3D CMR disease detection and propose Med-DualLoRA, a client-aware parameter-efficient fine-tuning (PEFT) federated framework that disentangles globally shared and local low-rank adaptations (LoRA) through additive decomposition. Global and local LoRA modules are trained locally, but only the global component is shared and aggregated across sites, keeping local adapters private. This design improves personalization while significantly reducing communication cost, and experiments show that adapting only two transformer blocks preserves performance while further improving efficiency. We evaluate our method on a multi-center state-of-the-art cine 3D CMR FM fine-tuned for disease detection using ACDC and combined M\&Ms datasets, treating each vendor as a federated client. Med-DualLoRA achieves statistically significant improved performance (balanced accuracy 0.768, specificity 0.612) compared to other federated PEFT baselines, while maintaining communication efficiency. Our approach provides a scalable solution for local federated adaptation of medical FMs under realistic clinical constraints.
Paper Structure (18 sections, 2 equations, 2 figures, 2 tables)

This paper contains 18 sections, 2 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Med-DualLora Framework
  3. Federated Dual Low-Rank Adaptation.
  4. Federated Optimization.
  5. Experimental Setup
  6. Datasets.
  7. Baselines.
  8. Federated Training Protocol.
  9. Implementation Details.
  10. Evaluation Metrics
  11. Communication Budget & LoRA Blocks Analysis.
  12. Results
  13. Centralized training provides an upper bound.
  14. Strong inter-client variability in local models.
  15. Standard PEFT degrades in FL.
  16. ...and 3 more sections

Figures (2)

  • Figure 1: (a) Fine-tuning baselines. (b) Our Med-DualLoRA method is a dual low-rank adaptation framework for collaborative learning of transferable global representations with reduced communication, while preserving client-specific differences under heterogeneous data. CLS: classifier head.
  • Figure 2: (a) Client-wise performance under centralized, client-specific, and federated training. Model performance is shown per client and averaged across clients (size-normalized); error bars indicate mean ± standard deviation across runs. (b) Pareto frontier of communication cost versus balanced accuracy. Stars denote frontier configurations, illustrating the trade-off between performance and communication per round.