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Guojun Tang

Guojun Tang

PhD Student in Electrical and Software Engineering, University of Calgary

Federated Learning

LinkedIn

Biography

Guojun Tang is a PhD student at DENOS Lab working on federated learning for health data. He is a co-author of COLA-GLM, the lab’s collaborative one-shot and lossless algorithm for generalized linear models on decentralized healthcare data, and his research addresses robustness under non-IID data and adversarial clients through adaptive aggregation and efficient client contribution computation. His work also spans blockchain-based federated learning.

Projects

  • COLA-GLM

    Collaborative one-shot and lossless algorithms of generalized linear models for decentralized observational healthcare data, in press at npj Digital Medicine.

  • Federated diabetes prediction

    Federated prediction of diabetes in Canadian adults using real-world cross-province primary care data.

Publications

  • npj Dig Med2025

    COLA-GLM: Collaborative one-shot and lossless algorithms of generalized linear models for decentralized observational healthcare data

    Qiong Wu, Jenna M. Reps, Lu Li, Guojun Tang, Steve Drew, Jiayu Zhou, David A. Asch, and Yong Chen

  • npj Dig Med2025

    Unlocking efficiency in real-world collaborative studies: a multi-site international study with one-shot lossless GLMM algorithm

    Guojun Tang, et al.

    paper

  • AMIA

    Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data

    Guojun Tang, et al.

    paper

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