mlsanityData to 7 Oct 2026

Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks

arXiv 2302.05675 ↗First posted 11 Feb 2023cs.LGcited by 19

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Abstract (arXiv)

Collaboration between healthcare institutions can significantly lessen the imbalance in medical resources across various geographic areas. However, directly sharing diagnostic information between institutions is typically not permitted due to the protection of patients' highly sensitive privacy. As a novel privacy-preserving machine learning paradigm, federated learning (FL) makes it possible to maximize the data utility among multiple medical institutions. These feature-enrichment FL techniques are referred to as vertical FL (VFL). Traditional VFL can only benefit multi-parties' shared samples, which strongly restricts its application scope. In order to improve the information-sharing capability and innovation of various healthcare-related institutions, and then to establish a next-generation open medical collaboration network, we propose a unified framework for vertical federated knowledge transfer mechanism (VFedTrans) based on a novel cross-hospital representation distillation component. Specifically, our framework includes three steps. First, shared samples' federated representations are extracted by collaboratively modeling multi-parties' joint features with current efficient vertical federated representation learning methods. Second, for each hospital, we learn a local-representation-distilled module, which can transfer the knowledge from shared samples' federated representations to enrich local samples' representations. Finally, each hospital can leverage local samples' representations enriched by the distillation module to boost arbitrary downstream machine learning tasks. The experiments on real-life medical datasets verify the knowledge transfer effectiveness of our framework.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 24 of its 64 references that are in the map. First posted on arXiv 11 Feb 2023.

Builds on

24 of 64 references.

References · 24

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2022Generative Adversarial Networkscited by 5,603
  4. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  5. 2021Model-Contrastive Federated Learningcited by 762
  6. 2019Federated Machine Learning: Concept and Applicationscited by 409
  7. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  8. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  9. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  10. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  11. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  12. 2021Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approachcited by 75
12 more
  1. 2020FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoringcited by 61
  2. 2021FLOP: Federated Learning on Medical Datasets using Partial Networkscited by 42
  3. 2022BlindFL: Vertical Federated Machine Learning without Peeking into Your Datacited by 36
  4. 2020FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Trainingcited by 32
  5. 2020An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learningcited by 18
  6. 2021PFA: Privacy-preserving Federated Adaptation for Effective Model Personalizationcited by 18
  7. 2021Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Datacited by 18
  8. 2022Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updatescited by 15
  9. 2021A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learningcited by 10
  10. 2022Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Datacited by 9
  11. 2022Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learningcited by 7
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 8; 2025: 7; 2026: 4.

19 papers.

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  1. 2024Federated Distillation: A Surveycited by 14
  2. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  3. 2024Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutionscited by 8
  4. 2024Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Datacited by 8
  5. 2024Vertical Federated Learning with Missing Features During Training and Inferencecited by 6
  6. 2024A Survey on Contribution Evaluation in Vertical Federated Learningcited by 5
  7. 2025UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaborationcited by 5
  8. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  9. 2024Towards Active Participant Centric Vertical Federated Learning: Some Representations May Be All You Needcited by 2
  10. 2025MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Taskscited by 2
  11. 2026Is the Trigger Essential? A Feature-Based Triggerless Backdoor Attack in Vertical Federated Learningcited by 1
  12. 2025Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarioscited by 0
7 more
  1. 2025On the Inference (In-)Security of Vertical Federated Learning: Efficient Auditing against Inference Tampering Attackcited by 0
  2. 2025Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Datacited by 0
  3. 2025X-VFL: A New Vertical Federated Learning Framework with Cross Completion and Decision Subspace Alignmentcited by 0
  4. 2025PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecturecited by 0
  5. 2026Mixture of Predefined Experts: Maximizing Data Usage on Vertical Federated Learningcited by 0
  6. 2026Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inferencecited by 0
  7. 2026Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practicecited by 0