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Related Experiment Videos

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Mengmeng Li1, Xin He2, Jinhua Chen2

  • 1College of Computer and Information Engineering, Henan University, Kaifeng, 475001, Henan, China; Henan International Joint Laboratory of Intelligent Network Theory and Key Technology, Henan University, Kaifeng, 475001, Henan, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 30, 2026
PubMed
Summary
This summary is machine-generated.

FCKMD enhances multimodal federated learning (MFL) for diverse data by adapting to missing modalities and client differences. This robust framework improves model generalization and prediction accuracy in challenging real-world scenarios.

Keywords:
Federated learningMissing modalitiesModality completionMultimodal learningNon-IID data

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Multimodal federated learning (MFL) trains global models from decentralized data without sharing private information.
  • Challenges in MFL include cross-client heterogeneity and missing modalities, leading to degraded performance.

Purpose of the Study:

  • To propose FCKMD, a robust MFL framework addressing heterogeneity and incomplete modalities.
  • To improve generalization and robustness in MFL under practical data conditions.

Main Methods:

  • Introduced a heterogeneity-adaptive modality expert encoding with a sample-wise router and bypass strategy.
  • Employed cross-modal reconstruction and reliability-aware constraints for incomplete data.
  • Developed a cross-view consistency transfer for knowledge distillation.

Main Results:

  • FCKMD demonstrated superior performance over baselines on CREMA-D, Crisis-MMD, and UCI-HAR datasets.
  • Achieved strong robustness and generalization across varying missing rates, heterogeneity levels, and client participation.

Conclusions:

  • FCKMD effectively handles cross-client heterogeneity and missing modalities in MFL.
  • The proposed framework offers a robust solution for real-world multimodal federated learning applications.