FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction

• URL: http://arxiv.org/abs/2112.10166v1
• Date: Sun, 19 Dec 2021 15:11:37 GMT
• Title: FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction
• Authors: Liang Peng, Nan Wang, Nicha Dvornek, Xiaofeng Zhu, Xiaoxiao Li
• Abstract summary: In medical applications, Graph Convolutional Neural Networks (GCNs) are widely used for graph analysis. GCNs rely on a vast amount of data, which is challenging to collect for a single medical institution. We propose a framework, FedNI, to leverage network inpainting and inter-institutional data via Federated Learning (FL)
• Score: 20.425551938050525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Convolutional Neural Networks (GCNs) are widely used for graph analysis. Specifically, in medical applications, GCNs can be used for disease prediction on a population graph, where graph nodes represent individuals and edges represent individual similarities. However, GCNs rely on a vast amount of data, which is challenging to collect for a single medical institution. In addition, a critical challenge that most medical institutions continue to face is addressing disease prediction in isolation with incomplete data information. To address these issues, Federated Learning (FL) allows isolated local institutions to collaboratively train a global model without data sharing. In this work, we propose a framework, FedNI, to leverage network inpainting and inter-institutional data via FL. Specifically, we first federatively train missing node and edge predictor using a graph generative adversarial network (GAN) to complete the missing information of local networks. Then we train a global GCN node classifier across institutions using a federated graph learning platform. The novel design enables us to build more accurate machine learning models by leveraging federated learning and also graph learning approaches. We demonstrate that our federated model outperforms local and baseline FL methods with significant margins on two public neuroimaging datasets.

Related papers

• TractGraphFormer: Anatomically Informed Hybrid Graph CNN-Transformer Network for Classification from Diffusion MRI Tractography [20.096952203108394]
  We introduce TractGraphFormer, a hybrid Graph CNN-Transformer deep learning framework tailored for diffusion MRI tractography. This model leverages local anatomical characteristics and global feature dependencies of white matter structures. In sex prediction tests, TractGraphFormer shows strong performance in large datasets of children and young adults.
  arXiv  Detail & Related papers  (2024-07-11T22:14:57Z)
• Federated Graph Semantic and Structural Learning [54.97668931176513]
  This paper reveals that local client distortion is brought by both node-level semantics and graph-level structure. We postulate that a well-structural graph neural network possesses similarity for neighbors due to the inherent adjacency relationships. We transform the adjacency relationships into the similarity distribution and leverage the global model to distill the relation knowledge into the local model.
  arXiv  Detail & Related papers  (2024-06-27T07:08:28Z)
• Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification [31.668499876984487]
  We propose a specificity-aware graph learning framework for rs-fMRI analysis and automated brain disorder identification. At each client, our model consists of a shared and a personalized branch, where parameters of the shared branch are sent to the server while those of the personalized branch remain local. Experimental results on two fMRI datasets with a total of 1,218 subjects suggest SFGL outperforms several state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-08-20T15:55:45Z)
• Distributed Learning over Networks with Graph-Attention-Based Personalization [49.90052709285814]
  We propose a graph-based personalized algorithm (GATTA) for distributed deep learning. In particular, the personalized model in each agent is composed of a global part and a node-specific part. By treating each agent as one node in a graph the node-specific parameters as its features, the benefits of the graph attention mechanism can be inherited.
  arXiv  Detail & Related papers  (2023-05-22T13:48:30Z)
• Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices [19.27111697495379]
  Graph neural networks (GNNs) have been widely deployed in real-world networked applications and systems. We propose the first federated GNN framework called Lumos that supports supervised and unsupervised learning. Based on the constructed tree for each client, a decentralized tree-based GNN trainer is proposed to support versatile training.
  arXiv  Detail & Related papers  (2023-03-01T13:27:06Z)
• Learning Graph Structure from Convolutional Mixtures [119.45320143101381]
  We propose a graph convolutional relationship between the observed and latent graphs, and formulate the graph learning task as a network inverse (deconvolution) problem. In lieu of eigendecomposition-based spectral methods, we unroll and truncate proximal gradient iterations to arrive at a parameterized neural network architecture that we call a Graph Deconvolution Network (GDN) GDNs can learn a distribution of graphs in a supervised fashion, perform link prediction or edge-weight regression tasks by adapting the loss function, and they are inherently inductive.
  arXiv  Detail & Related papers  (2022-05-19T14:08:15Z)
• Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications [52.65389473899139]
  Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Recent works have shown that considering relationships between input data samples have a positive regularizing effect for the downstream task. We propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications.
  arXiv  Detail & Related papers  (2022-04-01T10:01:37Z)
• Privatized Graph Federated Learning [57.14673504239551]
  We introduce graph federated learning, which consists of multiple units connected by a graph. We show how graph homomorphic perturbations can be used to ensure the algorithm is differentially private.
  arXiv  Detail & Related papers  (2022-03-14T13:48:23Z)
• Graph Neural Diffusion Networks for Semi-supervised Learning [6.376489604292251]
  Graph Convolutional Networks (GCN) is a pioneering model for graph-based semi-supervised learning. We propose a new graph neural network called neural-Nets (for Graph Neural Diffusion Networks) that exploits the local and global neighborhood information. The adoption of neural networks makes neural diffusions adaptable to different datasets.
  arXiv  Detail & Related papers  (2022-01-24T14:07:56Z)
• From Local Structures to Size Generalization in Graph Neural Networks [53.3202754533658]
  Graph neural networks (GNNs) can process graphs of different sizes. Their ability to generalize across sizes, specifically from small to large graphs, is still not well understood.
  arXiv  Detail & Related papers  (2020-10-17T19:36:54Z)
• Latent-Graph Learning for Disease Prediction [44.26665239213658]
  We show that it is possible to learn a single, optimal graph towards the GCN's downstream task of disease classification. Unlike commonly employed spectral GCN approaches, our GCN is spatial and inductive, and can thus infer previously unseen patients as well.
  arXiv  Detail & Related papers  (2020-03-27T08:18:01Z)

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.