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

• Published in: NPJ digital medicine Vol. 8; no. 1; pp. 442 - 11
• Main Authors: Wu, Qiong, Reps, Jenna M., Li, Lu, Zhang, Bingyu, Lu, Yiwen, Tong, Jiayi, Zhang, Dazheng, Lumley, Thomas, Brand, Milou T., Van Zandt, Mui, Falconer, Thomas, He, Xing, Huang, Yu, Li, Haoyang, Yan, Chao, Tang, Guojun, Williams, Andrew E., Wang, Fei, Bian, Jiang, Malin, Bradley, Hripcsak, George, Schuemie, Martijn J., Lu, Yun, Drew, Steve, Zhou, Jiayu, Asch, David A., Chen, Yong
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.07.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308; 692/499; Automation; Biomedicine; Biotechnology; Collaboration; COVID-19; Medicine; Medicine & Public Health; Privacy; United States > US
• ISSN: 2398-6352; 2398-6352
• DOI: 10.1038/s41746-025-01781-1

Clinical insights from real-world data often require aggregating information from institutions to ensure sufficient sample sizes and generalizability. However, patient privacy concerns only limit the sharing of patient-level data, and traditional federated learning algorithms, relying on extensive back-and-forth communications, can be inefficient to implement. We introduce the Collaborative One-shot Lossless Algorithm for Generalized Linear Models (COLA-GLM), a novel federated learning algorithm that supports diverse outcome types via generalized linear models and achieves results identical to a pooled patient-level data analysis ( lossless) with only a single round of aggregated data exchange ( one-shot ). To further protect aggregated institutional data, we developed a secure extension, secure-COLA-GLM, utilizing homomorphic encryption. We demonstrated the effectiveness and lossless property of COLA-GLM through applications to an international influenza cohort and a decentralized U.S. COVID-19 mortality study. COLA-GLM and secure-COLA-GLM offer a scalable, efficient solution for decentralized collaborative learning involving multiple data partners and diverse security requirements.