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7 results on '"Kersten, Kristopher"'

1. Empowering Federated Learning for Massive Models with NVIDIA FLARE

Author

Roth, Holger R., Xu, Ziyue, Hsieh, Yuan-Ting, Renduchintala, Adithya, Yang, Isaac, Zhang, Zhihong, Wen, Yuhong, Yang, Sean, Lu, Kevin, Kersten, Kristopher, Ricketts, Camir, Xu, Daguang, Chen, Chester, Cheng, Yan, and Feng, Andrew

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

In the ever-evolving landscape of artificial intelligence (AI) and large language models (LLMs), handling and leveraging data effectively has become a critical challenge. Most state-of-the-art machine learning algorithms are data-centric. However, as the lifeblood of model performance, necessary data cannot always be centralized due to various factors such as privacy, regulation, geopolitics, copyright issues, and the sheer effort required to move vast datasets. In this paper, we explore how federated learning enabled by NVIDIA FLARE can address these challenges with easy and scalable integration capabilities, enabling parameter-efficient and full supervised fine-tuning of LLMs for natural language processing and biopharmaceutical applications to enhance their accuracy and robustness.

Published
2024

2. NVIDIA FLARE: Federated Learning from Simulation to Real-World

Author

Roth, Holger R., Cheng, Yan, Wen, Yuhong, Yang, Isaac, Xu, Ziyue, Hsieh, Yuan-Ting, Kersten, Kristopher, Harouni, Ahmed, Zhao, Can, Lu, Kevin, Zhang, Zhihong, Li, Wenqi, Myronenko, Andriy, Yang, Dong, Yang, Sean, Rieke, Nicola, Quraini, Abood, Chen, Chester, Xu, Daguang, Ma, Nic, Dogra, Prerna, Flores, Mona, and Feng, Andrew

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Networking and Internet Architecture, Computer Science - Software Engineering
Abstract

Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVIDIA FLARE as an open-source software development kit (SDK) to make it easier for data scientists to use FL in their research and real-world applications. The SDK includes solutions for state-of-the-art FL algorithms and federated machine learning approaches, which facilitate building workflows for distributed learning across enterprises and enable platform developers to create a secure, privacy-preserving offering for multiparty collaboration utilizing homomorphic encryption or differential privacy. The SDK is a lightweight, flexible, and scalable Python package. It allows researchers to apply their data science workflows in any training libraries (PyTorch, TensorFlow, XGBoost, or even NumPy) in real-world FL settings. This paper introduces the key design principles of NVFlare and illustrates some use cases (e.g., COVID analysis) with customizable FL workflows that implement different privacy-preserving algorithms. Code is available at https://github.com/NVIDIA/NVFlare., Comment: Accepted at the International Workshop on Federated Learning, NeurIPS 2022, New Orleans, USA (https://federated-learning.org/fl-neurips-2022); Revised version v2: added Key Components list, system metrics for homomorphic encryption experiment; Extended v3 for journal submission

Published
2022
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3. Federated learning for predicting clinical outcomes in patients with COVID-19

Author

Dayan, Ittai, Roth, Holger R, Zhong, Aoxiao, Harouni, Ahmed, Gentili, Amilcare, Abidin, Anas Z, Liu, Andrew, Costa, Anthony Beardsworth, Wood, Bradford J, Tsai, Chien-Sung, Wang, Chih-Hung, Hsu, Chun-Nan, Lee, CK, Ruan, Peiying, Xu, Daguang, Wu, Dufan, Huang, Eddie, Kitamura, Felipe Campos, Lacey, Griffin, de Antônio Corradi, Gustavo César, Nino, Gustavo, Shin, Hao-Hsin, Obinata, Hirofumi, Ren, Hui, Crane, Jason C, Tetreault, Jesse, Guan, Jiahui, Garrett, John W, Kaggie, Joshua D, Park, Jung Gil, Dreyer, Keith, Juluru, Krishna, Kersten, Kristopher, Rockenbach, Marcio Aloisio Bezerra Cavalcanti, Linguraru, Marius George, Haider, Masoom A, AbdelMaseeh, Meena, Rieke, Nicola, Damasceno, Pablo F, e Silva, Pedro Mario Cruz, Wang, Pochuan, Xu, Sheng, Kawano, Shuichi, Sriswasdi, Sira, Park, Soo Young, Grist, Thomas M, Buch, Varun, Jantarabenjakul, Watsamon, Wang, Weichung, Tak, Won Young, Li, Xiang, Lin, Xihong, Kwon, Young Joon, Quraini, Abood, Feng, Andrew, Priest, Andrew N, Turkbey, Baris, Glicksberg, Benjamin, Bizzo, Bernardo, Kim, Byung Seok, Tor-Díez, Carlos, Lee, Chia-Cheng, Hsu, Chia-Jung, Lin, Chin, Lai, Chiu-Ling, Hess, Christopher P, Compas, Colin, Bhatia, Deepeksha, Oermann, Eric K, Leibovitz, Evan, Sasaki, Hisashi, Mori, Hitoshi, Yang, Isaac, Sohn, Jae Ho, Murthy, Krishna Nand Keshava, Fu, Li-Chen, de Mendonça, Matheus Ribeiro Furtado, Fralick, Mike, Kang, Min Kyu, Adil, Mohammad, Gangai, Natalie, Vateekul, Peerapon, Elnajjar, Pierre, Hickman, Sarah, Majumdar, Sharmila, McLeod, Shelley L, Reed, Sheridan, Gräf, Stefan, Harmon, Stephanie, Kodama, Tatsuya, Puthanakit, Thanyawee, Mazzulli, Tony, de Lavor, Vitor Lima, Rakvongthai, Yothin, Lee, Yu Rim, Wen, Yuhong, Gilbert, Fiona J, Flores, Mona G, and Li, Quanzheng

Subjects
Patient Safety, Good Health and Well Being, COVID-19, Electronic Health Records, Humans, Machine Learning, Outcome Assessment, Health Care, Prognosis, SARS-CoV-2, Medical and Health Sciences, Immunology
Abstract

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

Published
2021

4. Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals

Author

Peng, Le, primary, Luo, Gaoxiang, additional, Walker, Andrew, additional, Zaiman, Zachary, additional, Jones, Emma K, additional, Gupta, Hemant, additional, Kersten, Kristopher, additional, Burns, John L, additional, Harle, Christopher A, additional, Magoc, Tanja, additional, Shickel, Benjamin, additional, Steenburg, Scott D, additional, Loftus, Tyler, additional, Melton, Genevieve B, additional, Gichoya, Judy Wawira, additional, Sun, Ju, additional, and Tignanelli, Christopher J, additional

Published
2022
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5. Evaluation of Federated Learning Variations for COVID-19 diagnosis using Chest Radiographs from 42 US and European hospitals.

Author

Peng, Le, Luo, Gaoxiang, Walker, Andrew, Zaiman, Zachary, Jones, Emma K, Gupta, Hemant, Kersten, Kristopher, Burns, John L, Harle, Christopher A, Magoc, Tanja, Shickel, Benjamin, Steenburg, Scott D, Loftus, Tyler, Melton, Genevieve B, Gichoya, Judy Wawira, Sun, Ju, and Tignanelli, Christopher J

Abstract

Objective: Federated learning (FL) allows multiple distributed data holders to collaboratively learn a shared model without data sharing. However, individual health system data are heterogeneous. "Personalized" FL variations have been developed to counter data heterogeneity, but few have been evaluated using real-world healthcare data. The purpose of this study is to investigate the performance of a single-site versus a 3-client federated model using a previously described COVID-19 diagnostic model. Additionally, to investigate the effect of system heterogeneity, we evaluate the performance of 4 FL variations.Materials and Methods: We leverage a FL healthcare collaborative including data from 5 international healthcare systems (US and Europe) encompassing 42 hospitals. We implemented a COVID-19 computer vision diagnosis system using the FedAvg algorithm implemented on Clara Train SDK 4.0. To study the effect of data heterogeneity, training data was pooled from 3 systems locally and federation was simulated. We compared a centralized/pooled model, versus FedAvg, and 3 personalized FL variations (FedProx, FedBN, FedAMP).Results: We observed comparable model performance with respect to internal validation (local model: AUROC 0.94 vs FedAvg: 0.95, p = 0.5) and improved model generalizability with the FedAvg model (p < 0.05). When investigating the effects of model heterogeneity, we observed poor performance with FedAvg on internal validation as compared to personalized FL algorithms. FedAvg did have improved generalizability compared to personalized FL algorithms. On average, FedBN had the best rank performance on internal and external validation.Conclusion: FedAvg can significantly improve the generalization of the model compared to other personalization FL algorithms; however, at the cost of poor internal validity. Personalized FL may offer an opportunity to develop both internal and externally validated algorithms. [ABSTRACT FROM AUTHOR]

Published
2023
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6. Federated Learning used for predicting outcomes in SARS-COV-2 patients

Author

Flores, Mona, primary, Dayan, Ittai, additional, Roth, Holger, additional, Zhong, Aoxiao, additional, Harouni, Ahmed, additional, Gentili, Amilcare, additional, Abidin, Anas, additional, Liu, Andrew, additional, Costa, Anthony, additional, Wood, Bradford, additional, Tsai, Chien-Sung, additional, Wang, Chih-Hung, additional, Hsu, Chun-Nan, additional, Lee, CK, additional, Ruan, Colleen, additional, Xu, Daguang, additional, Wu, Dufan, additional, Huang, Eddie, additional, Kitamura, Felipe, additional, Lacey, Griffin, additional, Corradi, Gustavo César de Antônio, additional, Shin, Hao-Hsin, additional, Obinata, Hirofumi, additional, Ren, Hui, additional, Crane, Jason, additional, Tetreault, Jesse, additional, Guan, Jiahui, additional, Garrett, John, additional, Park, Jung Gil, additional, Dreyer, Keith, additional, Juluru, Krishna, additional, Kersten, Kristopher, additional, Rockenbach, Marcio Aloisio Bezerra Cavalcanti, additional, Linguraru, Marius, additional, Haider, Masoom, additional, AbdelMaseeh, Meena, additional, Rieke, Nicola, additional, Damasceno, Pablo, additional, Silva, Pedro Mario Cruz e, additional, Wang, Pochuan, additional, Xu, Sheng, additional, Kawano, Shuichi, additional, Sriswa, Sira, additional, Park, Soo Young, additional, Grist, Thomas, additional, Buch, Varun, additional, Jantarabenjakul, Watsamon, additional, Wang, Weichung, additional, Tak, Won Young, additional, Li, Xiang, additional, Lin, Xihong, additional, Kwon, Fred, additional, Gilbert, Fiona, additional, Kaggie, Josh, additional, Li, Quanzheng, additional, Quraini, Abood, additional, Feng, Andrew, additional, Priest, Andrew, additional, Turkbey, Baris, additional, Glicksberg, Benjamin, additional, Bizzo, Bernardo, additional, Kim, Byung Seok, additional, Tor-Diez, Carlos, additional, Lee, Chia-Cheng, additional, Hsu, Chia-Jung, additional, Lin, Chin, additional, Lai, Chiu-Ling, additional, Hess, Christopher, additional, Compas, Colin, additional, Bhatia, Deepi, additional, Oermann, Eric, additional, Leibovitz, Evan, additional, Sasaki, Hisashi, additional, Mori, Hitoshi, additional, Yang, Isaac, additional, Sohn, Jae Ho, additional, Murthy, Krishna Nand Keshava, additional, Fu, Li-Chen, additional, de Mendonça, Matheus Ribeiro Furtado, additional, Fralick, Mike, additional, Kang, Min Kyu, additional, Adil, Mohammad, additional, Gangai, Natalie, additional, Vateekul, Peerapon, additional, Elnajjar, Pierre, additional, Hickman, Sarah, additional, Majumdar, Sharmila, additional, McLeod, Shelley, additional, Reed, Sheridan, additional, Graf, Stefan, additional, Harmon, Stephanie, additional, Kodama, Tatsuya, additional, Puthanakit, Thanyawee, additional, Mazzulli, Tony, additional, Lavor, Vitor de Lima, additional, Rakvongthai, Yothin, additional, Lee, Yu Rim, additional, and Wen, Yuhong, additional

Published
2021
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7. Federated Learning used for predicting outcomes in SARS-COV-2 patients.

Author

Flores M, Dayan I, Roth H, Zhong A, Harouni A, Gentili A, Abidin A, Liu A, Costa A, Wood B, Tsai CS, Wang CH, Hsu CN, Lee CK, Ruan C, Xu D, Wu D, Huang E, Kitamura F, Lacey G, César de Antônio Corradi G, Shin HH, Obinata H, Ren H, Crane J, Tetreault J, Guan J, Garrett J, Park JG, Dreyer K, Juluru K, Kersten K, Bezerra Cavalcanti Rockenbach MA, Linguraru M, Haider M, AbdelMaseeh M, Rieke N, Damasceno P, Cruz E Silva PM, Wang P, Xu S, Kawano S, Sriswasdi S, Park SY, Grist T, Buch V, Jantarabenjakul W, Wang W, Tak WY, Li X, Lin X, Kwon F, Gilbert F, Kaggie J, Li Q, Quraini A, Feng A, Priest A, Turkbey B, Glicksberg B, Bizzo B, Kim BS, Tor-Diez C, Lee CC, Hsu CJ, Lin C, Lai CL, Hess C, Compas C, Bhatia D, Oermann E, Leibovitz E, Sasaki H, Mori H, Yang I, Sohn JH, Keshava Murthy KN, Fu LC, Furtado de Mendonça MR, Fralick M, Kang MK, Adil M, Gangai N, Vateekul P, Elnajjar P, Hickman S, Majumdar S, McLeod S, Reed S, Graf S, Harmon S, Kodama T, Puthanakit T, Mazzulli T, de Lima Lavor V, Rakvongthai Y, Lee YR, and Wen Y

Abstract

'Federated Learning' (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the "EXAM" (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.

Published
2021
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