# Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches

arXiv: https://arxiv.org/abs/2311.03417 · HTML: https://mlsanity.com/paper/2311.03417/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems](https://mlsanity.com/paper/2006.07856/) (2020)
- [Benchmarking emergency department triage prediction models with machine learning and large public electronic health records](https://mlsanity.com/paper/2111.11017/) (2021)
- [Federated Tensor Factorization for Computational Phenotyping](https://mlsanity.com/paper/1704.03141/) (2017)
- [Augmented Transfer Regression Learning with Semi-non-parametric Nuisance Models](https://mlsanity.com/paper/2010.02521/) (2020)
- [Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints](https://mlsanity.com/paper/2004.00816/) (2020)
- [A synthetic data integration framework to leverage external summary-level information from heterogeneous populations](https://mlsanity.com/paper/2106.06835/) (2021)
- [FedScore: A privacy-preserving framework for federated scoring system development](https://mlsanity.com/paper/2303.00282/) (2023)
- [Federated and distributed learning applications for electronic health records and structured medical data: A scoping review](https://mlsanity.com/paper/2304.07310/) (2023)

## Built on by (most cited first, 8 of 8)

- [BioMamba: Domain-Adaptive Biomedical Language Models](https://mlsanity.com/paper/2408.02600/) (2024)
- [FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction](https://mlsanity.com/paper/2410.17269/) (2024)
- [Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data](https://mlsanity.com/paper/2403.05229/) (2024)
- [Shift-Invariant Attribute Scoring for Kolmogorov-Arnold Networks via Shapley Value](https://mlsanity.com/paper/2510.01663/) (2025)
- [Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction](https://mlsanity.com/paper/2605.24212/) (2026)
- [Communication-efficient distributed hazard difference estimation for heterogeneous multi-site survival data](https://mlsanity.com/paper/2601.14609/) (2026)
- [mtslearn: Machine Learning in Python for Medical Time Series](https://mlsanity.com/paper/2603.29432/) (2026)
- [Trans-RAG: Query-Centric Vector Transformation for Secure Cross-Organizational Retrieval](https://mlsanity.com/paper/2604.09541/) (2026)

## Abstract (arXiv)

Federated learning (FL) has shown promising potential in safeguarding data privacy in healthcare collaborations. While the term "FL" was originally coined by the engineering community, the statistical field has also explored similar privacy-preserving algorithms. Statistical FL algorithms, however, remain considerably less recognized than their engineering counterparts. Our goal was to bridge the gap by presenting the first comprehensive comparison of FL frameworks from both engineering and statistical domains. We evaluated five FL frameworks using both simulated and real-world data. The results indicate that statistical FL algorithms yield less biased point estimates for model coefficients and offer convenient confidence interval estimations. In contrast, engineering-based methods tend to generate more accurate predictions, sometimes surpassing central pooled and statistical FL models. This study underscores the relative strengths and weaknesses of both types of methods, emphasizing the need for increased awareness and their integration in future FL applications.