Enabling Global Image Data Sharing in the Life Sciences

no code implementations • 23 Jan 2024 • Peter Bajcsy, Sreenivas Bhattiprolu, Katy Boerner, Beth A Cimini, Lucy Collinson, Jan Ellenberg, Reto Fiolka, Maryellen Giger, Wojtek Goscinski, Matthew Hartley, Nathan Hotaling, Rick Horwitz, Florian Jug, Anna Kreshuk, Emma Lundberg, Aastha Mathur, Kedar Narayan, Shuichi Onami, Anne L. Plant, Fred Prior, Jason Swedlow, Adam Taylor, Antje Keppler

Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences.

APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service

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.

Federated Learning Privacy Preserving