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

cs.CVMar 11, 2026

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

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

Organizations: Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain · Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

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.

Figures & tables

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.

	Canon		GE		Philips		Siemens		ACDC		Total
	DIS	NOR	DIS	NOR	DIS	NOR	DIS	NOR	DIS	NOR	DIS	NOR
Train	28	7	53	20	78	26	151	16	72	18	382	87
Val	3	4	14	1	15	7	32	3	8	2	72	17
Test	5	3	15	2	19	4	34	3	40	10	113	22
Total	36	14	82	23	112	37	217	22	120	30	567	126

Table 1: Distribution of diseased (DIS) and non-diseased (NOR) samples across clients and splits, including overall totals.

Centralized
Experiment	Balanced Acc.	Sensitivity	Specificity	F1
Full-ft	0.745 ± 0.049 ${}^{~~~}$	0.953 ± 0.013 ${}^{~~}$	0.538 ± 0.111 ${}^{~~}$	0.927 ± 0.006
Head-only	0.810 ± 0.124 ${}^{~~~}$	0.949 ± 0.006 ${}^{~~}$	0.672 ± 0.250 ${}^{~~}$	0.937 ± 0.022
LoRA	0.780 ± 0.042 ${}^{~~~}$	0.943 ± 0.017 ${}^{~~}$	0.616 ± 0.092 ${}^{~~}$	0.929 ± 0.009
Federated
Head-only	0.514 ± 0.024 ∗∗	0.997 ± 0.005 ∗	0.030 ± 0.053 ∗∗	0.909 ± 0.000

Table 2: Performance metrics (mean ± std) for centralized and federated baselines, and Med-DualLoRA. Asterisks indicate statistically significant differences compared to Med-DualLoRA using paired t-test: ∗ p<0.05, ∗∗ p<0.01.

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.

Federated Fine-Tuning

Federated fine-tuning adapts pretrained models across decentralized data holders without pooling their private data.

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FM $^2$ : Unified Federated Foundation Models for Heterogeneous Multimodal Medical Imaging

Shengchao Chen, Ting Shu

School of Artificial Intelligence, Shenzhen University Shenzhen, China · Australian AI Institute, University of Technology Sydney Sydney, Australia

Multimodal Federated Learning Cross-Modal Alignment Medical VQA Medical Image Classification Multimodal Foundation Models Federated Learning+1

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Building foundation models for medical imaging requires pooling data across institutions, yet privacy regulations prohibit centralized aggregation. Existing Federated Foundation Models either fine-tune natural-image models with poor medical-domain transfer, or train from scratch within a single modality, lacking the flexibility to unify tasks. We identify an under-explored challenge, Imaging Modality Heterogeneity, where clients operate under two structural regimes: Overlapped (shared modalities with heterogeneous label distributions) and Non-overlapped (fully disjoint modalities per client). We propose FM

^2

, a unified framework that trains the core backbone from scratch to preserve medical domain fidelity while optionally incorporating biomedical pretrained encoders for vision-language alignment. FM

^2

equips each client with dual Mixture-of-Experts modules (a Class-wise MoE for personalized category knowledge and a Domain-wise MoE for shared cross-modality representations), coupled with a Heterogeneous Modality Alignment (HMA) regularizer that explicitly aligns modality-specific expert parameters, admitting provable

O(1/\sqrt{T})

convergence and generalization guarantees. FM

^2

further incorporates Caption-Enhanced Learning (CEL), where locally retained GPT-4o-generated captions serve as a textual semantic bridge enabling representation transfer across clients with disjoint modalities, and demonstrates extensibility to Federated Medical VQA. Experiments on our MIMH benchmark (classification and CEL) and real-world medical VQA datasets confirm consistent superiority over state-of-the-art federated baselines and strong out-of-modality generalization across all three tasks.

Abstract

Federated Fine-Tuning

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