FedCoRe: Target-Adaptive Completion for Missing Modalities in Healthcare Federated Learning
- URL: http://arxiv.org/abs/2608.18311v1
- Date: Tue, 18 Aug 2026 20:45:23 GMT
- Title: FedCoRe: Target-Adaptive Completion for Missing Modalities in Healthcare Federated Learning
- Abstract summary: Federated multimodal models often assume every site has every modality, although hospitals differ in access to EHRs, chest radiographs, and ECGs.<n>We study this setting on a MIMIC-derived respiratory deterioration task with simulated FL clients and introduce FedCoRe.<n>FedCoRe learns representation- or logit-space corrections rather than generating synthetic ECGs or CXR images.
- Score: 3.616090348351236
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Federated multimodal models often assume every site has every modality, although hospitals differ in access to EHRs, chest radiographs, and ECGs. We study this setting on a MIMIC-derived respiratory deterioration task with simulated FL clients and introduce FedCoRe (Federated Cross-Modal Representation Completion). FedCoRe learns representation- or logit-space corrections rather than generating synthetic ECGs or CXR images. When a client observes a modality that may be missing at deployment, it evaluates the same example with and without that modality to obtain paired supervision. Only clients with such pairs update the completion module, and validation may retain the unchanged prediction. We freeze the trained multimodal predictor during evaluation so that measured differences come only from completion. Hiding ECG reduced AUROC by about 0.085; paired-example FedAvg restored 0.0415 AUROC, or 49.0% of the lost performance. We therefore report two distinct effects: paired-example FedAvg partially recovers the missing-ECG gap, while validation-selected completion is a task-specific classifier-logit correction rather than literal ECG recovery. For CXR, effect-aware completion recovers 52.8% of the loss in a controlled test where CXR is hidden. Paired-example FedAvg transfers part of this effect, but validation keeps the no-completion baseline for deployment cases whose inputs lack CXR. Thus, FedCoRe should be read as a validation-gated completion/correction framework: it can recover missing-modality signal in supported settings, but it should be deployed only when paired examples and validation evidence support that modality.
Related papers
- Conditioning noise is a free regularizer for LoRA fine-tuning: no pathology encoder required for diffusion-based artifact detection in histopathology [1.8864137667201766]
Diffusion-based artifact detectors score whole-slide image patches by reconstruction error under a model fine-tuned on clean tissue.<n>We show that conditioning this fine-tuning on random Gaussian embeddings consistently widens the clean/artifact separation.
arXiv Detail & Related papers (2026-09-11T07:16:38Z) - CARD: Calibration via Agreement in Reverse Diffusion for Out-of-Domain MRI Segmentation [14.844162865265673]
We propose CARD, which maps the temporal aggregate of disagreement to a temperature field applied per pixel across all classes, so that confidence changes while the segmentation does not.<n>Across cardiac, prostate and brain MRI shifts, CARD lowers calibration error in 45 of 49 comparisons against the strongest baseline in each setting.
arXiv Detail & Related papers (2026-08-26T07:23:08Z) - Cross-Fitted Residual Utility for Primary-Preserving Cognitive Decision Correction in Automatic Modulation Classification [1.547549252134621]
We study a post-inference problem through cross-fitted residual utility and a primary-preserving cognitive decision policy.<n>A structured KAN-Fourier classifier supplies the default probability, while neural and non-neural candidates provide observable evidence.<n>On RMLA, RMLB, and HISAR, the complete system improves overall accuracy from 63.632% to 66.332%, 65.161% to 66.168%, and 77.769% to 79.867%.
arXiv Detail & Related papers (2026-08-03T11:07:37Z) - Loud or Silent? A Reusable Framework for Per-Modality Failure Analysis in Multimodal Clinical AI [3.7524666944787644]
Two questions matter beyond the size of the accuracy loss: which modality was responsible, and whether the model fails loudly or silently once that modality is dropped.<n>We present a model-agnostic modality-failure framework: given N modality embeddings, any mask-aware probe, and labels, it returns a per-example failure taxonomy.<n>It reports per-modality loud-vs-silent rates, and scales to a three-modality complementarity matrix.
arXiv Detail & Related papers (2026-08-02T19:38:56Z) - DriftScope: Measuring The Hidden Effects of Diffusion Model Adaptation [50.584834483403675]
Adapting pre-trained text-to-image diffusion models, whether to learn new visual concepts or erase unwanted ones, is routinely evaluated on its intended effects alone.<n>We demonstrate that adaptation systematically damages semantically unrelated concepts in ways that aggregate metrics structurally cannot surface.<n>We introduce DriftScope, a prompt-level diagnostic tool that takes any two model checkpoints and returns a ranked list of tokens whose visual concepts have shifted most between them.
arXiv Detail & Related papers (2026-06-30T20:57:51Z) - Explaining RhythmFormer: A Systematic XAI Analysis of Periodic Sparse Attention for Remote Photoplethysmography [1.647210198730602]
Photoplethys transformers achieve low heart-rate error on benchmarks, yet their decisions remain opaque.<n>Existing XAI is dominated by qualitative heatmap inspection without quantitative faithfulness metrics or physiology-grounded validation.<n>We adapt four methods (raw attention, rollout, flow, Beyond Intuition) to quantifying RhythmFormer's bi-level attention with top-$k$ selection.
arXiv Detail & Related papers (2026-06-11T19:10:06Z) - A Controlled Audit of Pretraining Contamination in Public Medical Vision-Language Benchmarks [46.43161388634735]
Medical vision-language models (VLMs) are evaluated on public benchmarks whose images and question-answer pairs have been freely downloadable for years.<n>We audit open VLMs on SLAKE-En, PathVQA, VQA-RAD, and an auxiliary public OmniMedVQA mirror using four detector families.
arXiv Detail & Related papers (2026-06-08T18:40:10Z) - Rethinking Incompleteness: Formalizing Protocol Divergence and Train-Once Learning for Robust IMVC [13.498020856656494]
IMVC evaluation retrains separate models for different missing-data configurations.<n>We show that this paradigm obscures a fundamental vulnerability: missing rate alone is insufficient to characterize data incompleteness.<n>We formalize this phenomenon as incompleteness divergence, providing measures that capture structural disparities across missing-data protocols.
arXiv Detail & Related papers (2026-06-03T13:24:09Z) - Testing the Test: Score-Direction Instability in Class-Split Anomaly Detection [56.362776482614976]
Within-dataset class-split evaluation is widely used as a proxy for fully unconditional out-of-distribution anomaly detection.<n>We show that this protocol can become ill-posed when the held-out anomaly class overlaps the normal mixture in representation space.<n>We introduce a simple training-free diagnostic, neighborhood class leakage, and show that it predicts score-direction instability across Fashion-MNIST, CIFAR-10, and Imagenette.
arXiv Detail & Related papers (2026-05-23T06:27:42Z) - ANNEAL: Adapting LLM Agents via Governed Symbolic Patch Learning [12.44509691077682]
We introduce ANNEAL, a neuro-symbolic agent that converts recurring failures into governed symbolic edits of a process knowledge graph.<n>Its core mechanism, Failure-Driven Knowledge Acquisition (FDKA), localizes the responsible operator, synthesizes a typed patch through constrained LLM generation, and validates the proposal.<n>Across four domains and 27 multi-seed runs, ANNEAL is the only evaluated system that commits persistent structural repairs.
arXiv Detail & Related papers (2026-05-04T05:24:03Z) - CARE What Fails: Contrastive Anchored-REflection for Verifiable Multimodal [84.71254539482369]
Group-relative reinforcement learning with verifiable rewards (RLVR) often wastes the most informative data it already has the failures.<n>We present CARE, a failure-centric post-training framework for multimodal reasoning that turns errors into supervision.<n> CARE improves accuracy and training smoothness while explicitly increasing the share of learning signal that comes from failures.
arXiv Detail & Related papers (2025-12-22T16:34:21Z) - Predicting Fetal Outcomes from Cardiotocography Signals Using a Supervised Variational Autoencoder [1.8352113484137627]
We develop and interpret a supervised variational autoencoder (VAE) model for classifying cardiotocography (CTG) signals based on pregnancy outcomes.
arXiv Detail & Related papers (2025-09-08T10:54:04Z) - Mind the Gap: Bridging Occlusion in Gait Recognition via Residual Gap Correction [57.75544235445363]
We propose RG-Gait, a method for residual correction for occluded gait recognition with holistic retention.<n>Our proposed network adaptively integrates the learned residual, significantly improving performance on occluded gait sequences.
arXiv Detail & Related papers (2025-07-15T04:45:14Z) - Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation [63.180725016463974]
Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice.
We introduce a novel noisy correspondence learning framework, namely textbfSelf-textbfReinforcing textbfErrors textbfMitigation (SREM)
arXiv Detail & Related papers (2023-12-27T09:03:43Z)
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.