Federated Tensor Factorization for Computational Phenotyping
Open accessAbstract
Tensor factorization models offer an effective approach to convert massive electronic health records into meaningful clinical concepts (phenotypes) for data analysis. These models need a large amount of diverse samples to avoid population bias. An open challenge is how to derive phenotypes jointly across multiple hospitals, in which direct patient-level data sharing is not possible (e.g., due to institutional policies). In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data. We developed secure data harmonization and federated computation procedures based on alternating direction method of multipliers (ADMM). Using this method, the multiple hospitals iteratively update tensors and transfer secure summarized information to a central server, and the server aggregates the information to generate phenotypes. We demonstrated with real medical datasets that our method resembles the centralized training model (based on combined datasets) in terms of accuracy and phenotypes discovery while respecting privacy.
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Byzantine clients can send arbitrary values instead of locally updated models, which can result in convergence to sub-optimal models or divergence.
“Instead of sending locally updated models to the server, these Byzantine clients can send arbitrary values. This can result in convergence to sub-optimal models, or even lead to divergence [76].”
From Advances and Open Problems in Federated Learning (2021) · cites this paper as [76]
Section 4.3.2 describes hospitals sending locally updated factor matrices to the server. But the paper’s privacy analysis (§4.5) assumes an honest-but-curious adversary; our copy does not address Byzantine clients sending arbitrary values or whether that causes sub-optimal convergence or divergence. Page numbers are unavailable in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredFederated learning has been applied in electronic health records to represent and find clinically similar patients, predict hospitalisations due to cardiac events, mortality and ICU stay time; in medical imaging for whole-brain segmentation in MRI and brain tumour segmentation; for fMRI classification to find disease-related biomarkers; and suggested as a promising approach for COVID-19.
“In the context of electronic health records (EHR), for example, FL helps to represent and to find clinically similar patients13,47, as well as predicting hospitalisations due to cardiac events14, mortality and ICU stay time19.”
From The future of digital health with federated learning (2020) · cites this paper as [47]
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Experimental — accuracy not yet measuredVarious robust aggregators are provably effective for Byzantine-tolerant distributed learning under appropriate assumptions, even in federated settings.
“Past work has shown that various robust aggregators are provably effective for Byzantine-tolerant distributed learning [436, 76, 116] under appropriate assumptions, even in federated settings [379, 486, 427].”
From Advances and Open Problems in Federated Learning (2021) · cites this paper as [76]
Section 4.4 proves convergence of the paper’s federated tensor-factorization method, not Byzantine tolerance. The Introduction discusses robustness to sampling error from small or unevenly distributed hospitals; a search for Byzantine or malicious participants found no passage addressing whether robust aggregators are effective against them. (LLM, conf=0.78)
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- Authors
- Kim, Y., Sun, J., Yu, H., & Jiang, X.
- Affiliations
- Pohang University of Science and Technology (KR)
- Georgia Institute of Technology (US)
- University of California San Diego (US)
- Venue
- Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2017-08-04
- Volume
- 2017
- Pages
- 887-895
- PMC
- PMC5652331
- OpenAlex
- W2606065148
- Cited by
- 128
- References
- 33
- Citations by year
- 2026: 5 · 2025: 17 · 2024: 24 · 2023: 18 · 2022: 19 · 2021: 19 · 2020: 15 · 2019: 5 · 2018: 5 · 2017: 1
- FWCI
- 3.24
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Ministry of Science ICT and Future Planning (2014-0-00147)
- National Institutes of Health (R01GM118609, R21LM012060)
- National Research Foundation of Korea (2016R1E1A1A01942642)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Mathematics › Computational Mathematics › Tensor decomposition and applications
- Keywords
- computational phenotyping, electronic health records, data privacy, data harmonization, alternating direction method of multipliers, population bias, privacy-preserving machine learning
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