Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data Analysis
Background: Federated learning (FL) holds promise for safeguarding data privacy in healthcare collaborations. While the term “FL” was originally coined by the engineering community, the statistical field has also developed privacy-preserving algorithms, though these are less recognized. Our goal was...
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2024-01-01
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| Series: | Health Data Science |
| Online Access: | https://spj.science.org/doi/10.34133/hds.0196 |
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| author | Siqi Li Di Miao Qiming Wu Chuan Hong Danny D’Agostino Xin Li Yilin Ning Yuqing Shang Ziwen Wang Molei Liu Huazhu Fu Marcus Eng Hock Ong Hamed Haddadi Nan Liu |
| author_facet | Siqi Li Di Miao Qiming Wu Chuan Hong Danny D’Agostino Xin Li Yilin Ning Yuqing Shang Ziwen Wang Molei Liu Huazhu Fu Marcus Eng Hock Ong Hamed Haddadi Nan Liu |
| author_sort | Siqi Li |
| collection | DOAJ |
| description | Background: Federated learning (FL) holds promise for safeguarding data privacy in healthcare collaborations. While the term “FL” was originally coined by the engineering community, the statistical field has also developed privacy-preserving algorithms, though these are less recognized. Our goal was to bridge this gap with the first comprehensive comparison of FL frameworks from both domains. Methods: We assessed 7 FL frameworks, encompassing both engineering-based and statistical FL algorithms, and compared them against local and centralized modeling of logistic regression and least absolute shrinkage and selection operator (Lasso). Our evaluation utilized both simulated data and real-world emergency department data, focusing on comparing both estimated model coefficients and the performance of model predictions. Results: The findings reveal that statistical FL algorithms produce much less biased estimates of model coefficients. Conversely, engineering-based methods can yield models with slightly better prediction performance, occasionally outperforming both centralized and statistical FL models. Conclusion: This study underscores the relative strengths and weaknesses of both types of methods, providing recommendations for their selection based on distinct study characteristics. Furthermore, we emphasize the critical need to raise awareness of and integrate these methods into future applications of FL within the healthcare domain. |
| format | Article |
| id | doaj-art-485d4c145ea1486c82b147ca621ae86a |
| institution | OA Journals |
| issn | 2765-8783 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Health Data Science |
| spelling | doaj-art-485d4c145ea1486c82b147ca621ae86a2025-08-20T02:38:32ZengAmerican Association for the Advancement of Science (AAAS)Health Data Science2765-87832024-01-01410.34133/hds.0196Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data AnalysisSiqi Li0Di Miao1Qiming Wu2Chuan Hong3Danny D’Agostino4Xin Li5Yilin Ning6Yuqing Shang7Ziwen Wang8Molei Liu9Huazhu Fu10Marcus Eng Hock Ong11Hamed Haddadi12Nan Liu13Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA.Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore, Singapore.Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.Department of Computing, Imperial College London, London, England, UK.Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.Background: Federated learning (FL) holds promise for safeguarding data privacy in healthcare collaborations. While the term “FL” was originally coined by the engineering community, the statistical field has also developed privacy-preserving algorithms, though these are less recognized. Our goal was to bridge this gap with the first comprehensive comparison of FL frameworks from both domains. Methods: We assessed 7 FL frameworks, encompassing both engineering-based and statistical FL algorithms, and compared them against local and centralized modeling of logistic regression and least absolute shrinkage and selection operator (Lasso). Our evaluation utilized both simulated data and real-world emergency department data, focusing on comparing both estimated model coefficients and the performance of model predictions. Results: The findings reveal that statistical FL algorithms produce much less biased estimates of model coefficients. Conversely, engineering-based methods can yield models with slightly better prediction performance, occasionally outperforming both centralized and statistical FL models. Conclusion: This study underscores the relative strengths and weaknesses of both types of methods, providing recommendations for their selection based on distinct study characteristics. Furthermore, we emphasize the critical need to raise awareness of and integrate these methods into future applications of FL within the healthcare domain.https://spj.science.org/doi/10.34133/hds.0196 |
| spellingShingle | Siqi Li Di Miao Qiming Wu Chuan Hong Danny D’Agostino Xin Li Yilin Ning Yuqing Shang Ziwen Wang Molei Liu Huazhu Fu Marcus Eng Hock Ong Hamed Haddadi Nan Liu Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data Analysis Health Data Science |
| title | Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data Analysis |
| title_full | Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data Analysis |
| title_fullStr | Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data Analysis |
| title_full_unstemmed | Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data Analysis |
| title_short | Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data Analysis |
| title_sort | federated learning in healthcare a benchmark comparison of engineering and statistical approaches for structured data analysis |
| url | https://spj.science.org/doi/10.34133/hds.0196 |
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