# Federated Tensor Factorization for Computational Phenotyping

arXiv: https://arxiv.org/abs/1704.03141 · HTML: https://mlsanity.com/paper/1704.03141/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2017.

## Built on by (most cited first, 21 of 21)

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- [Transformers in Healthcare: A Survey](https://mlsanity.com/paper/2307.00067/) (2023)
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- [FedMood: Federated Learning on Mobile Health Data for Mood Detection](https://mlsanity.com/paper/2102.09342/) (2021)
- [Towards the Practical Utility of Federated Learning in the Medical Domain](https://mlsanity.com/paper/2207.03075/) (2022)
- [Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health](https://mlsanity.com/paper/2108.10761/) (2021)
- [Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches](https://mlsanity.com/paper/2311.03417/) (2023)
- [Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data](https://mlsanity.com/paper/2202.03758/) (2022)
- [Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorization](https://mlsanity.com/paper/2311.10341/) (2023)
- [Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions](https://mlsanity.com/paper/2209.00322/) (2022)
- [Federated and distributed learning applications for electronic health records and structured medical data: A scoping review](https://mlsanity.com/paper/2304.07310/) (2023)
- [Protecting Big Data Privacy Using Randomized Tensor Network Decomposition and Dispersed Tensor Computation](https://mlsanity.com/paper/2101.04194/) (2021)
- [Honest Score Client Selection Scheme: Preventing Federated Learning Label Flipping Attacks in Non-IID Scenarios](https://mlsanity.com/paper/2311.05826/) (2023)
- [FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on Federated Learning](https://mlsanity.com/paper/2404.11890/) (2024)
- [Federated learning, ethics, and the double black box problem in medical AI](https://mlsanity.com/paper/2504.20656/) (2025)
- [Communication Efficient Generalized Tensor Factorization for Decentralized Healthcare Networks](https://mlsanity.com/paper/2109.01718/) (2021)
- [Federated Learning based Latent Factorization of Tensors for Privacy-Preserving QoS Prediction](https://mlsanity.com/paper/2407.19828/) (2024)

## Abstract (arXiv)

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.