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Your search keyword '"Beutel, Daniel J."' showing total 3 results
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3 results on '"Beutel, Daniel J."'

1. Federated benchmarking of medical artificial intelligence with MedPerf

Author

Karargyris, Alexandros, Umeton, Renato, Sheller, Micah J., Aristizabal, Alejandro, George, Johnu, Wuest, Anna, Pati, Sarthak, Kassem, Hasan, Zenk, Maximilian, Baid, Ujjwal, Narayana Moorthy, Prakash, Chowdhury, Alexander, Guo, Junyi, Nalawade, Sahil, Rosenthal, Jacob, Kanter, David, Xenochristou, Maria, Beutel, Daniel J., Chung, Verena, Bergquist, Timothy, Eddy, James, Abid, Abubakar, Tunstall, Lewis, Sanseviero, Omar, Dimitriadis, Dimitrios, Qian, Yiming, Xu, Xinxing, Liu, Yong, Goh, Rick Siow Mong, Bala, Srini, Bittorf, Victor, Puchala, Sreekar Reddy, Ricciuti, Biagio, Samineni, Soujanya, Sengupta, Eshna, Chaudhari, Akshay, Coleman, Cody, Desinghu, Bala, Diamos, Gregory, Dutta, Debo, Feddema, Diane, Fursin, Grigori, Huang, Xinyuan, Kashyap, Satyananda, Lane, Nicholas, Mallick, Indranil, Mascagni, Pietro, Mehta, Virendra, Moraes, Cassiano Ferro, Natarajan, Vivek, Nikolov, Nikola, Padoy, Nicolas, Pekhimenko, Gennady, Reddi, Vijay Janapa, Reina, G. Anthony, Ribalta, Pablo, Singh, Abhishek, Thiagarajan, Jayaraman J., Albrecht, Jacob, Wolf, Thomas, Miller, Geralyn, Fu, Huazhu, Shah, Prashant, Xu, Daguang, Yadav, Poonam, Talby, David, Awad, Mark M., Howard, Jeremy P., Rosenthal, Michael, Marchionni, Luigi, Loda, Massimo, Johnson, Jason M., Bakas, Spyridon, and Mattson, Peter

Published
2023
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2. MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation

Author

Karargyris, Alexandros, Umeton, Renato, Sheller, Micah J., Aristizabal, Alejandro, George, Johnu, Bala, Srini, Beutel, Daniel J., Bittorf, Victor, Chaudhari, Akshay, Chowdhury, Alexander, Coleman, Cody, Desinghu, Bala, Diamos, Gregory, Dutta, Debo, Feddema, Diane, Fursin, Grigori, Guo, Junyi, Huang, Xinyuan, Kanter, David, Kashyap, Satyananda, Lane, Nicholas, Mallick, Indranil, Mascagni, Pietro, Mehta, Virendra, Natarajan, Vivek, Nikolov, Nikola, Padoy, Nicolas, Pekhimenko, Gennady, Reddi, Vijay Janapa, Reina, G Anthony, Ribalta, Pablo, Rosenthal, Jacob, Singh, Abhishek, Thiagarajan, Jayaraman J., Wuest, Anna, Xenochristou, Maria, Xu, Daguang, Yadav, Poonam, Rosenthal, Michael, Loda, Massimo, Johnson, Jason M., and Mattson, Peter

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

Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience. We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data. To meet this need, we are building MedPerf, an open framework for benchmarking machine learning in the medical domain. MedPerf will enable federated evaluation in which models are securely distributed to different facilities for evaluation, thereby empowering healthcare organizations to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status, and our roadmap. We call for researchers and organizations to join us in creating the MedPerf open benchmarking platform.

Published
2021
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3. A First Look into the Carbon Footprint of Federated Learning.

Author

Xinchi Qiu, Parcollet, Titouan, Fernandez-Marques, Javier, Gusmao, Pedro P. B., Yan Gao, Beutel, Daniel J., Topal, Taner, Mathur, Akhil, and Lane, Nicholas D.

Subjects
*FEDERATED learning, *ECOLOGICAL impact, *DEEP learning, *CARBON emissions, *SERVER farms (Computer network management), *SOCIAL groups
Abstract

Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers. In response, alternatives to centralized training such as Federated Learning (FL) have emerged. FL is now starting to be deployed at a global scale by companies that must adhere to new legal demands and policies originating from governments and social groups advocating for privacy protection. However, the potential environmental impact related to FL remains unclear and unexplored. This article offers the first-ever systematic study of the carbon footprint of FL. We propose a rigorous model to quantify the carbon footprint, hence facilitating the investigation of the relationship between FL design and carbon emissions. We also compare the carbon footprint of FL to traditional centralized learning. Our findings show that, depending on the configuration, FL can emit up to two orders of magnitude more carbon than centralized training. However, in certain settings, it can be comparable to centralized learning due to the reduced energy consumption of embedded devices. Finally, we highlight and connect the results to the future challenges and trends in FL to reduce its environmental impact, including algorithms efficiency, hardware capabilities, and stronger industry transparency. [ABSTRACT FROM AUTHOR]

Published
2023

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