mlsanityData to 14 Oct 2026

Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification

arXiv 2308.10302 ↗First posted 20 Aug 2023q-bio.QM cs.LG eess.SPcited by 4

Not on the map. Why?

Abstract (arXiv)

Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive approach to examining abnormal brain connectivity associated with brain disorders. Graph neural network (GNN) gains popularity in fMRI representation learning and brain disorder analysis with powerful graph representation capabilities. Training a general GNN often necessitates a large-scale dataset from multiple imaging centers/sites, but centralizing multi-site data generally faces inherent challenges related to data privacy, security, and storage burden. Federated Learning (FL) enables collaborative model training without centralized multi-site fMRI data. Unfortunately, previous FL approaches for fMRI analysis often ignore site-specificity, including demographic factors such as age, gender, and education level. To this end, we propose a specificity-aware federated graph learning (SFGL) framework for rs-fMRI analysis and automated brain disorder identification, with a server and multiple clients/sites for federated model aggregation and prediction. 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. This can facilitate knowledge sharing among sites and also helps preserve site specificity. In the shared branch, we employ a spatio-temporal attention graph isomorphism network to learn dynamic fMRI representations. In the personalized branch, we integrate vectorized demographic information (i.e., age, gender, and education years) and functional connectivity networks to preserve site-specific characteristics. Representations generated by the two branches are then fused for classification. Experimental results on two fMRI datasets with a total of 1,218 subjects suggest that SFGL outperforms several state-of-the-art approaches.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 25 of its 78 references that are in the map. First posted on arXiv 20 Aug 2023.

Builds on

25 of 78 references.

References · 25

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2017Graph Attention Networkscited by 8,923
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  5. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  6. 2018How Powerful are Graph Neural Networks?cited by 4,393
  7. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  8. 2014Striving for Simplicity: The All Convolutional Netcited by 990
  9. 2021Model-Contrastive Federated Learningcited by 766
  10. 2020A Survey on Visual Transformercited by 654
  11. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  12. 2017Exploring Generalization in Deep Learningcited by 600
13 more
  1. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 325
  2. 2020Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Resultscited by 122
  3. 2022Federated Learning with Partial Model Personalizationcited by 114
  4. 2016Neural Photo Editing with Introspective Adversarial Networkscited by 109
  5. 2021Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attentioncited by 87
  6. 2022FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generationcited by 67
  7. 2020Spatio-Temporal Graph Convolution for Resting-State fMRI Analysiscited by 57
  8. 2020Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysiscited by 41
  9. 2021FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Predictioncited by 26
  10. 2018Machine learning in resting-state fMRI analysiscited by 25
  11. 2022Deep reinforcement learning guided graph neural networks for brain network analysiscited by 12
  12. 2022Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Predictioncited by 6
  13. 2022Benchmarking Graph Neural Networks for FMRI analysiscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024STARFormer: A Novel Spatio-Temporal Aggregation Reorganization Transformer of FMRI for Brain Disorder Diagnosiscited by 2
  2. 2025FedSKD: Aggregation-free Model-heterogeneous Federated Learning via Multi-dimensional Similarity Knowledge Distillation for Medical Image Classificationcited by 1
  3. 2024SpaRG: Sparsely Reconstructed Graphs for Generalizable fMRI Analysiscited by 0
  4. 2026FedDOSE: Federated Learning Framework Decomposing Site Effects for Modeling Brain Dynamic Functional Connectivitycited by 0