Add paper

paper/paper.bib

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+@article{mcmahan2017communication,
+  title={Communication-efficient learning of deep networks from decentralized data},
+  author={McMahan, Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and others},
+  journal={Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS)},
+  year={2017},
+  pages={1273--1282},
+  url={https://arxiv.org/abs/1602.05629},
+  publisher={PMLR}
+}
+
+@article{rieke2020future,
+  title={The future of digital health with federated learning},
+  author={Rieke, Nicola and Hancox, Jon and Li, Wenqi and Milletari, Fausto and Roth, Holger R and Albarqouni, Shadi and Bakas, Spyridon and Galtier, Mathieu N and Landman, Bennett A and Maier-Hein, Klaus and others},
+  journal={npj Digital Medicine},
+  volume={3},
+  number={1},
+  pages={119},
+  year={2020},
+  doi={10.1038/s41746-020-00323-1},
+  publisher={Nature Publishing Group}
+}
+
+
+
+@article{Fedorov:2017,
+  author = {{Fedorov}, A. and {Johnson}, J. and {Damaraju}, E. and {Ozerin}, A. and {Calhoun}, V. and {Plis}, S.},
+  title = {End-to-end learning of brain tissue segmentation from imperfect labeling},
+  journal = {IEEE International Joint Conference on Neural Networks (IJCNN)},
+  doi = {10.1109/IJCNN.2017.7966333},
+  year = {2017},
+  month = {May}
+}
+
+@article{Yu:2016,
+  author = {{Yu}, F. and {Koltun}, V.},
+  title = {Multi-scale context aggregation by dilated convolutions},
+  journal = {arXiv},
+  archiveprefix = {arXiv},
+  doi = {10.48550/arXiv.1511.07122},
+  year = {2016},
+  month = {Apr}
+}
+
+@article{nvflare,
+  title={NVFlare: NVIDIA Federated Learning Application Runtime Environment},
+  author={NVIDIA},
+  journal={GitHub Repository},
+  year={2023},
+  url={https://github.com/NVIDIA/NVFlare}
+}
+
+
+
+@article{mindboggle,
+  title={Mindboggle: Automated human brain image labeling and morphometry},
+  author={Klein, Arno and Tourville, Jason},
+  journal={Frontiers in Neuroinformatics},
+  volume={6},
+  pages={2},
+  year={2012},
+  doi={10.3389/fninf.2012.00002},
+  publisher={Frontiers}
+}
+
+
+@misc{meshfl_repo,
+  title={MeshFL},
+  year={2025},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  url={https://github.com/Mmasoud1/MeshFL},
+}
+
+@article{credit,
+  author={Brand, Amy and Allen, Liz and Altman, Micah and Hlava, Marjorie and Scott, Jo},
+  title={Beyond authorship: attribution, contribution, collaboration, and credit},
+  journal={Learned Publishing},
+  volume={28},
+  number={2},
+  pages={151--155},
+  year={2015},
+  doi={10.1087/20150211},
+  url={https://onlinelibrary.wiley.com/doi/abs/10.1087/20150211}
+}

paper/paper.md

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+---
+title: 'MeshFL: A Decentralized Framework for Federated Learning in Neuroimaging'
+tags:
+    - Federated Learning
+    - Distributed Machine Learning
+    - Neuroimaging
+    - MRI Segmentation
+    - MeshNet
+authors:
+    - name: Mohamed Masoud^[corresponding author]
+      orcid: 0000-0002-5365-242X
+      affiliation: 1
+    - name: Sergey Plis
+      orcid: 0000-0003-0040-0365
+      affiliation: "1, 2"
+    - name: [authors]
+      affiliation: 1
+affiliations:
+  - name: Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, United States of America
+    index: 1
+  - name: Department of Computer Science, Georgia State University, Atlanta, United States of America
+    index: 2
+date: 8 January 2025
+bibliography: paper.bib
+---
+
+# Summary
+
+Advancements in federated learning have paved the way for privacy-preserving collaborative training of machine learning models on decentralized datasets. This is particularly relevant in fields like neuroimaging, where sensitive data, such as brain MRI scans, cannot be easily shared across institutions. MeshFL [@meshfl_repo] is an open-source framework designed to facilitate distributed training of deep learning models for 3D brain MRI segmentation while maintaining data privacy. Built upon NVFlare [@nvflare], MeshFL employs federated learning principles to train MeshNet models [@Fedorov:2017] across multiple data sites, enabling high-accuracy segmentation of white and gray matter regions. With Dice scores of ~0.92 for training and ~0.9 for validation, MeshFL demonstrates that decentralized training can achieve performance comparable to centralized setups.
+
+# Statement of Need
+
+In neuroimaging, collaborative machine learning is often hindered by the sensitive nature of patient data and the computational demands of training large 3D models. Traditional centralized learning approaches require aggregating data in one location, which is impractical for datasets governed by strict privacy laws. Federated learning addresses this limitation by enabling model training without sharing raw data between sites [@mcmahan2017communication, @rieke2020future].
+
+Existing federated learning frameworks often lack specific adaptations for neuroimaging tasks. MeshFL fills this gap by offering:
+
+- A tailored framework for 3D brain MRI segmentation using the MeshNet model.
+- Integration with NVFlare for federated training workflows [@nvflare].
+- Support for heterogeneously distributed data across sites.
+
+MeshFL provides an easy-to-use yet robust environment for researchers and clinicians, ensuring high model performance while preserving patient privacy.
+
+# Implementation
+
+MeshFL leverages NVFlare to implement federated learning workflows, allowing local sites to independently train the MeshNet model on their data and exchange model updates with a central server as shown in \autoref{fig:MeshFL-Seq-Diagram}. 
+
+
+
+MeshFL key features include:
+- **Data Preprocessing:** Automated partitioning of MRI scans into training, validation, and testing sets.
+- **Model Training:** The framework utilizes PyTorch for implementing the MeshNet model and optimize memory usage. Layer checkpointing further reduces memory overhead during training.
+- **Aggregation Strategies:** Federated averaging of model weights to produce a global model. Initial model weights are shared across sites for consistent training initialization.
+- **Custom Logger:** MeshFL includes a GenericLogger for detailed logging of training progress, gradient application, and Dice score evaluations.
+- **Scalability:** Seamless support for multiple sites with varying data distributions and qualities.
+
+The architecture of MeshNet, a 3D convolutional neural network, is optimized for volumetric brain MRI segmentation, employing dilated convolutions to capture contextual information while maintaining a compact parameter set [@Yu:2016]. A CrossEntropyLoss criterion with class weights addresses class imbalance.
+
+MeshFL also integrates a learning rate scheduler to enhance training stability. Using OneCycleLR, the scheduler gradually increases the learning rate during the initial phase of training and decreases it afterward, ensuring convergence without disrupting the learning process. This approach prevents spikes in the learning rate and supports optimal parameter updates.
+
+
+# Validation
+
+The performance of MeshFL was validated using the Mindboggle dataset [@mindboggle] on 15 MRI samples labeled for white and gray matter segmentation. Using Dice coefficient as the evaluation metric, MeshFL achieved comparable accuracy to centralized training setups while adhering to federated learning constraints. Benchmarks were conducted with uniformly distributed data across sites.
+
+Results demonstrated that MeshFL achieved Dice scores of ~0.92 for training and ~0.9 for validation with robust performance comparable to centralized training  \autoref{fig:MeshFL-Performance}.
+
+
+# Code Availability
+
+MeshFL is openly available on GitHub at [https://github.com/Mmasoud1/MeshFL](https://github.com/Mmasoud1/MeshFL). The repository includes comprehensive documentation, example scripts, and a wiki to guide users through installation and usage. Researchers can reproduce the experiments described here or adapt MeshFL for their applications.
+
+# Author Contributions
+
+We describe contributions to this paper using the CRediT taxonomy [@credit].
+- **Writing – Original Draft:** M.M.
+- **Writing – Review & Editing:** M.M., and S.P.
+- **Conceptualization and Methodology:** M.M., and S.P.
+- **Software and Data Curation:** M.M.
+- **Validation:** M.M., and S.P.
+- **Resources:** [To be added]
+- **Project Administration:** M.M.
+
+# Acknowledgments
+
+This work was funded by the NIH grant xxxx.
+
+# References