UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaboration

• URL: http://arxiv.org/abs/2501.11388v1
• Date: Mon, 20 Jan 2025 10:33:07 GMT
• Title: UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaboration
• Authors: Chung-ju Huang, Yuanpeng He, Xiao Han, Wenpin Jiao, Zhi Jin, Leye Wang,
• Abstract summary: Cross-hospital collaboration has the potential to address disparities in medical resources across different regions. Strict privacy regulations prohibit the direct sharing of sensitive patient information between hospitals.
• Score: 23.16178059724539
• License:
• Abstract: Cross-hospital collaboration has the potential to address disparities in medical resources across different regions. However, strict privacy regulations prohibit the direct sharing of sensitive patient information between hospitals. Vertical federated learning (VFL) offers a novel privacy-preserving machine learning paradigm that maximizes data utility across multiple hospitals. Traditional VFL methods, however, primarily benefit patients with overlapping data, leaving vulnerable non-overlapping patients without guaranteed improvements in medical prediction services. While some knowledge transfer techniques can enhance the prediction performance for non-overlapping patients, they fall short in addressing scenarios where overlapping and non-overlapping patients belong to different domains, resulting in challenges such as feature heterogeneity and label heterogeneity. To address these issues, we propose a novel unified vertical federated knowledge transfer framework (Unitrans). Our framework consists of three key steps. First, we extract the federated representation of overlapping patients by employing an effective vertical federated representation learning method to model multi-party joint features online. Next, each hospital learns a local knowledge transfer module offline, enabling the transfer of knowledge from the federated representation of overlapping patients to the enriched representation of local non-overlapping patients in a domain-adaptive manner. Finally, hospitals utilize these enriched local representations to enhance performance across various downstream medical prediction tasks. Experiments on real-world medical datasets validate the framework's dual effectiveness in both intra-domain and cross-domain knowledge transfer. The code of \method is available at \url{https://github.com/Chung-ju/Unitrans}.

Related papers

• Comparing Federated Stochastic Gradient Descent and Federated Averaging for Predicting Hospital Length of Stay [0.0]
  Predicting hospital length of stay (LOS) reliably is an essential need for efficient resource allocation at hospitals. Traditional predictive modeling tools frequently have difficulty acquiring sufficient and diverse data because healthcare institutions have privacy rules in place. This modeling approach facilitates collaborative model training by modeling decentralized data sources from different hospitals without extracting sensitive data outside of hospitals.
  arXiv  Detail & Related papers  (2024-07-17T17:00:20Z)
• Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity [4.84693589377679]
  We propose Federated Distributionally Robust Alignment (FedDRA) for medical vision-language pre-training. FedDRA achieves robust vision-language alignment under heterogeneous conditions. Our method also adapts well to various medical pre-training methods.
  arXiv  Detail & Related papers  (2024-04-05T01:17:25Z)
• Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency [10.16245019262119]
  Federated learning aims to train a shared model of isolated clients without local data exchange. In this work, we propose a novel federated semi-supervised learning framework for medical image segmentation.
  arXiv  Detail & Related papers  (2024-03-19T12:52:38Z)
• Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks [9.121410198690088]
  We propose a unified framework for vertical federated knowledge transfer mechanism (VFedTrans) based on a novel cross-hospital representation distillation component. 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. Experiments on real-life medical datasets verify the knowledge transfer effectiveness of our framework.
  arXiv  Detail & Related papers  (2023-02-11T12:15:37Z)
• Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation [103.55894890759376]
  This paper introduces several building blocks that use representation learning to handle the heterogeneous feature spaces. We show how these building blocks can be used to recover transfer learning equivalents of the standard CATE learners.
  arXiv  Detail & Related papers  (2022-10-08T16:41:02Z)
• When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning Framework in Classification of Medical Images on Limited Data: A COVID-19 Case Study [77.34726150561087]
  COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. CNN has been widely utilized and verified in analyzing medical images.
  arXiv  Detail & Related papers  (2022-03-24T02:09:41Z)
• Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases [57.90226879210227]
  FedCy is a semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos. We demonstrate significant performance gains over state-of-the-art FSSL methods on the task of automatic recognition of surgical phases.
  arXiv  Detail & Related papers  (2022-03-14T17:44:53Z)
• Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching [58.26619456972598]
  Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. This paper studies a practical yet challenging FL problem, named textitFederated Semi-supervised Learning (FSSL) We present a novel approach for this problem, which improves over traditional consistency regularization mechanism with a new inter-client relation matching scheme.
  arXiv  Detail & Related papers  (2021-06-16T07:58:00Z)
• FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space [63.43592895652803]
  Federated learning allows distributed medical institutions to collaboratively learn a shared prediction model with privacy protection. While at clinical deployment, the models trained in federated learning can still suffer from performance drop when applied to completely unseen hospitals outside the federation. We present a novel approach, named as Episodic Learning in Continuous Frequency Space (ELCFS), for this problem. The effectiveness of our method is demonstrated with superior performance over state-of-the-arts and in-depth ablation experiments on two medical image segmentation tasks.
  arXiv  Detail & Related papers  (2021-03-10T13:05:23Z)
• Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization [59.5104563755095]
  We introduce a simple but effective approach to improve the generalization capability of deep neural networks in the field of medical imaging classification. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a representative feature space through variational encoding.
  arXiv  Detail & Related papers  (2020-09-27T12:30:30Z)

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.