Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation

• URL: http://arxiv.org/abs/2606.08687v1
• Date: Sun, 07 Jun 2026 15:39:28 GMT
• Title: Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation
• Abstract summary: Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of segmentation foundation models for medical imaging.<n>Most federated LoRA methods adopt a uniform aggregation rule, which breaks under the encoder-decoder asymmetry in medical segmentation.<n>This mismatch entangles shared anatomy with site-specific biases and harms generalization.<n>We introduce a Subspace Orthogonality Regularizer that penalizes shared-local collinearity in the effective update space.
• Score: 60.41721951405564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of segmentation foundation models for medical imaging. However, most federated LoRA methods adopt a uniform aggregation rule, which breaks under the encoder-decoder asymmetry in medical segmentation: the encoder is dominated by appearance shifts, while the decoder is dominated by supervision variations. This mismatch entangles shared anatomy with site-specific biases and harms generalization. To address this, we propose Inverse Asymmetric Tuning (IAT). IAT aligns adaptation with heterogeneity sources by personalizing module-specific components in the encoder to absorb appearance shifts and in the decoder to accommodate site-dependent supervision, while retaining a shared pathway for transferable consensus. However, structural separation alone is insufficient under LoRA's bilinear parameterization, where multiplicative coupling can still cause site-specific updates to leak into the shared direction. We therefore introduce a Subspace Orthogonality Regularizer that penalizes shared-local collinearity in the effective update space, mitigating leakage without extra communication. Experiments show consistent improvements over strong federated LoRA and parameter-efficient FL baselines.

Related papers

• SeGDeP: Semantic- and Geometric-Aware Decoupled Prompts for Reasoning Segmentation [51.46828526392219]
  Reasoning segmentation converts an implicit linguistic conclusion into a precise mask.<n>Existing MLLM-segmenter interfaces either use a special trigger or compress both signals into one context.<n>We present SeGDeP, an explicit what-where interface.
  arXiv  Detail & Related papers  (2026-09-08T15:11:32Z)
• FAN-LoRA: A Fourier-Adaptive Nonlinear Low-Rank Adaptor for Medical Foundation Model Domain Adaptation [11.04143971119602]
  We present FAN-LoRA, a novel frequency-decoupled fine-tuning architecture.<n>We show that FAN-LoRA consistently outperforms state-of-the-art PEFT baselines.<n>Compared to the strongest competitors, our method achieves consistent improvements in average Dice scores and notable reductions in boundary errors.
  arXiv  Detail & Related papers  (2026-08-27T02:08:57Z)
• Rank-Aware Hyperbolic Alignment for Vision-Language Dataset Distillation [54.74045834035952]
  Vision-language dataset distillation (VLDD) compresses a large image-text paired dataset into a small set of synthetic pairs.<n>We propose a rank-aware hyperbolic alignment (RAHA) that combines hierarchical geometry with explicit alignment-capacity control.<n>RAHA demonstrates competitive cross-modal retrieval and improved transfer indicators under fixed budgets.
  arXiv  Detail & Related papers  (2026-06-28T15:41:31Z)
• PermDoRA -- Understanding Adapter Interference in Language Models: Limits of Parameter-Space Geometry [0.0]
  Common hypothesis is that interference during adapter composition arises from overlap in linear parameter updates.<n>We test this hypothesis using DoRA-RBAC, a hierarchical adapter composition framework based on weight-decomposed low-rank adaptation.<n>Our results show that while single-domain performance matches LoRA, geometry-aware merging provides no consistent advantage over standard averaging in multi-domain settings.
  arXiv  Detail & Related papers  (2026-06-09T02:52:48Z)
• Federated LoRA Fine-Tuning for LLMs via Collaborative Alignment [4.733680065352959]
  Low-rank adaptation (LoRA) has emerged as a powerful tool for parameter-efficient fine-tuning of large language models (LLMs)<n>This paper studies LoRA under a federated learning setting, enabling collaborative fine-tuning across clients while preserving parameter efficiency.<n>We propose Collaborative Low-rank Alignment and Identifiable Recovery (CLAIR), a contamination-aware framework that relies only on preliminary local estimators.
  arXiv  Detail & Related papers  (2026-05-20T14:12:16Z)
• Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs [59.973833105841685]
  A key challenge for improving adversarial transferability is to effectively capture the intrinsic visual focus shared across different models.<n>We propose FRA-Attack, which addresses both challenges from a unified frequency-domain regularization perspective.<n>For feature alignment, a high-pass DCT objective on patch features suppresses redundant global structures and concentrates the loss on the high-frequency band that carries the MLLMs' intrinsic visual focus.<n>For gradient optimization, we introduce Frequency-domain Gradient Regularization (FGR), a textitmodel-agnostic low-pass regularizer that modulates the surrogate gradient using
  arXiv  Detail & Related papers  (2026-05-20T08:15:56Z)
• AHPA: Adaptive Hierarchical Prior Alignment for Diffusion Transformers [24.937985157569823]
  We argue that such timestep-agnostic alignment is suboptimal because the useful granularity of representation supervision changes systematically with the signal-to-noise ratio.<n>In high-noise regimes, diffusion models benefit more from coarse semantic and layout-level anchoring, whereas in low-noise regimes, the training signal should emphasize spatially detailed and structurally faithful refinement.<n>This non-stationary alignment behavior creates a representational mismatch for static single-level supervisors.
  arXiv  Detail & Related papers  (2026-05-05T03:07:29Z)
• DARC: Disagreement-Aware Alignment via Risk-Constrained Decoding [59.16244104797919]
  We propose **Disagreement-Aware Alignment via Risk-Constrained Decoding (DARC)**.<n>DARC frames response selection as distributionally robust, risk-sensitive decision making.<n>Experiments on alignment benchmarks show that DARC reduces disagreement and tail risk while maintaining competitive average quality.
  arXiv  Detail & Related papers  (2026-03-09T09:21:29Z)
• Diffusion Controller: Framework, Algorithms and Parameterization [54.82539154511621]
  We present a unified control-theoretic view that casts reverse diffusion sampling as state-only control within (generalized) linearly-solvable Markov Decision Processes.<n>Under this framework, control acts by reweighting the pretrained reverse-time transition kernels, balancing terminal objectives against an $f$-divergence cost.<n>Experiments on Stable Diffusion v1.4 show consistent gains in preference-alignment win rates and improved quality-efficiency trade-offs.
  arXiv  Detail & Related papers  (2026-03-07T01:49:59Z)
• When Domain Pretraining Interferes with Instruction Alignment: An Empirical Study of Adapter Merging in Medical LLMs [0.6345523830122167]
  Large language models can exhibit surprising adapter interference when combining domain adaptation and instruction alignment.<n>We study a two-stage LoRA pipeline for medical LLMs, where domain-oriented pre-training (PT) and supervised fine-tuning (SFT) are trained separately and later merged.
  arXiv  Detail & Related papers  (2026-01-26T10:54:06Z)
• Rethinking Inter-LoRA Orthogonality in Adapter Merging: Insights from Orthogonal Monte Carlo Dropout [26.252083958846203]
  Low-Rank Adaptation (LoRA), a popular fine-tuning method for large models, typically trains a module to represent a specific concept such as an object or a style.<n>We propose Orthogonal Monte Carlo Dropout, a mechanism that enforces strict orthogonality when combining sparse semantic vectors without extra time complexity.
  arXiv  Detail & Related papers  (2025-09-26T18:44:03Z)
• Regularizing Subspace Redundancy of Low-Rank Adaptation [54.473090597164834]
  We propose ReSoRA, a method that explicitly models redundancy between mapping subspaces and adaptively Regularizes Subspace redundancy of Low-Rank Adaptation.<n>Our proposed method consistently facilitates existing state-of-the-art PETL methods across various backbones and datasets in vision-language retrieval and standard visual classification benchmarks.<n>As a training supervision, ReSoRA can be seamlessly integrated into existing approaches in a plug-and-play manner, with no additional inference costs.
  arXiv  Detail & Related papers  (2025-07-28T11:52:56Z)
• Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation [1.121358474059223]
  Federated learning (FL) enables multiple sites to collaboratively train powerful deep models without compromising data privacy and security. Weakly supervised segmentation, which uses sparsely-grained supervision, is increasingly being paid attention to due to its great potential of reducing annotation costs. We propose a novel personalized FL framework for medical image segmentation, named FedICRA, which uniformly leverages heterogeneous weak supervision.
  arXiv  Detail & Related papers  (2023-04-12T06:32:08Z)
• Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation [51.93711960601973]
  We propose a novel margin preserving self-paced contrastive Learning model for cross-modal medical image segmentation. With the guidance of progressively refined semantic prototypes, a novel margin preserving contrastive loss is proposed to boost the discriminability of embedded representation space. Experiments on cross-modal cardiac segmentation tasks demonstrate that MPSCL significantly improves semantic segmentation performance.
  arXiv  Detail & Related papers  (2021-03-15T15:23:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.