no code implementations • 19 Feb 2024 • Zilinghan Li, Shilan He, Pranshu Chaturvedi, Volodymyr Kindratenko, Eliu A Huerta, Kibaek Kim, Ravi Madduri
Federated learning enables multiple data owners to collaboratively train robust machine learning models without transferring large or sensitive local datasets by only sharing the parameters of the locally trained models.
1 code implementation • 26 Sep 2023 • Zilinghan Li, Pranshu Chaturvedi, Shilan He, Han Chen, Gagandeep Singh, Volodymyr Kindratenko, E. A. Huerta, Kibaek Kim, Ravi Madduri
Nonetheless, because of the disparity of computing resources among different clients (i. e., device heterogeneity), synchronous federated learning algorithms suffer from degraded efficiency when waiting for straggler clients.
1 code implementation • 17 Aug 2023 • Zilinghan Li, Shilan He, Pranshu Chaturvedi, Trung-Hieu Hoang, Minseok Ryu, E. A. Huerta, Volodymyr Kindratenko, Jordan Fuhrman, Maryellen Giger, Ryan Chard, Kibaek Kim, Ravi Madduri
Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e. g., healthcare of financial) local data.
no code implementations • 6 Oct 2021 • Xinkai Yuan, Zilinghan Li, Gaoang Wang
With human-in-the-loop, active learning can iteratively select informative unlabeled samples for labeling and training to improve the performance in the SSL framework.