mlsanityData to 14 Oct 2026

HybridVFL: Disentangled Feature Learning for Edge-Enabled Vertical Federated Multimodal Classification

arXiv 2512.10701 ↗First posted 11 Dec 2025cs.LGcited by 0

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Abstract (arXiv)

Vertical Federated Learning (VFL) offers a privacy-preserving paradigm for Edge AI scenarios like mobile health diagnostics, where sensitive multimodal data reside on distributed, resource-constrained devices. Yet, standard VFL systems often suffer performance limitations due to simplistic feature fusion. This paper introduces HybridVFL, a novel framework designed to overcome this bottleneck by employing client-side feature disentanglement paired with a server-side cross-modal transformer for context-aware fusion. Through systematic evaluation on the multimodal HAM10000 skin lesion dataset, we demonstrate that HybridVFL significantly outperforms standard federated baselines, validating the criticality of advanced fusion mechanisms in robust, privacy-preserving systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 36 references that are in the map. First posted on arXiv 11 Dec 2025.

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References · 19

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  5. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,013
  6. 2018Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representationscited by 941
  7. 2021Model-Contrastive Federated Learningcited by 766
  8. 2019Multimodal Transformer for Unaligned Multimodal Language Sequencescited by 645
  9. 2019Federated Machine Learning: Concept and Applicationscited by 412
  10. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  11. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 176
  12. 2023Federated Learning for Medical Image Analysis: A Surveycited by 78
7 more
  1. 2021CAFE: Catastrophic Data Leakage in Vertical Federated Learningcited by 76
  2. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  3. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  4. 2024Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selectioncited by 11
  5. 2023Cross-Modal Vertical Federated Learning for MRI Reconstructioncited by 9
  6. 2024Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentationcited by 8
  7. 2023A Distributed Privacy Preserving Model for the Detection of Alzheimer's Diseasecited by 1

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