Abstract image of a network

Federated Learning Collaborative

What is federated learning?

About Us

We are a community of health data science and AI researchers who are facilitating:

  1. Research collaboration and study design
  2. Joint multi-center healthcare data science grants
  3. Development and validation of AI and machine learning healthcare algorithms
Importance of Collaboratives

A multi-center healthcare data collaborative that leverages federated learning offers several significant benefits, particularly when it focuses on research collaboration, joint grants, and the development and validation of AI and machine learning algorithms. These benefits include better data security, enhanced research collaboration, and more robust AI models.

Learn more
Collaborate with Us

If you would like to get involved with or participate in the collaborative, email:

Chris Tignanelli
[email protected]

Marley Crews Hill
[email protected]

Our Collaborators

Mock up of site logos

Map of where each site is located

Our Leadership Team

University of Minnesota
Chris Tignanelli, Genevieve Melton-Meaux

University of Florida
Tyler Loftus

Emory University
Judy Wawira Gichoya

Indiana University
John Burns

The Medical University of South Carolina
Heather Evans

University of North Carolina at Chapel Hill
Michael Phillips

Publications


2022
Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals

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

Federated learning for preserving data privacy in collaborative healthcare research

Tyler J Loftus, Matthew M Ruppert, Benjamin Shickel, Tezcan Ozrazgat-Baslanti, Jeremy A Balch, Philip A Efron, Gilbert R Upchurch Jr, Parisa Rashidi, Christopher Tignanelli, Jiang Bian 7, Azra Bihorac

Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study

Ju Sun, Le Peng, Taihui Li, Dyah Adila, Zach Zaiman, Genevieve B Melton-Meaux, Nicholas E Ingraham, Eric Murray, Daniel Boley, Sean Switzer, John L Burns, Kun Huang, Tadashi Allen, Scott D Steenburg, Judy Wawira Gichoya, Erich Kummerfeld, Christopher J Tignanelli

AI recognition of patient race in medical imaging: a modelling study

Judy Wawira Gichoya, Imon Banerjee, Ananth Reddy Bhimireddy, John L Burns, Leo Anthony Celi, Li-Ching Chen, Ramon Correa, Natalie Dullerud, Marzyeh Ghassemi, Shih-Cheng Huang, Po-Chih Kuo, Matthew P Lungren, Lyle J Palmer, Brandon J Price, Saptarshi Purkayastha, Ayis T Pyrros, Lauren Oakden-Rayner, Chima Okechukwu, Laleh Seyyed-Kalantari, Hari Trivedi, Ryan Wang, Zachary Zaiman, Haoran Zhang