Personalized federated learning for in-hospital mortality prediction of multi-center ICU
Federated learning (FL), as a paradigm for addressing challenges of machine learning (ML) to be applied in private distributed data provides a novel and promising scheme to promote ML in multiple independently distributed healthcare institutions. However, the non-IID and unbalanced nature of the dat...
| Main Authors: | , , , |
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| Format: | Article |
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Institute of Electrical and Electronics Engineers
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/109394/ |
| _version_ | 1848865359268937728 |
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| author | Deng, Ting Hamdan, Hazlina Yaakob, Razali Kasmiran, Khairul Azhar |
| author_facet | Deng, Ting Hamdan, Hazlina Yaakob, Razali Kasmiran, Khairul Azhar |
| author_sort | Deng, Ting |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Federated learning (FL), as a paradigm for addressing challenges of machine learning (ML) to be applied in private distributed data provides a novel and promising scheme to promote ML in multiple independently distributed healthcare institutions. However, the non-IID and unbalanced nature of the data distribution can decrease its performance, even resulting in the institutions losing motivation to participate in its training. This paper explored the problem with an in-hospital mortality prediction task under an actual multi-center ICU electronic health record database that preserves the original non-IID and unbalanced data distribution. It first analyzed the reason for the performance degradation of baseline FL under this data scenario, and then proposed a personalized FL (PFL) approach named POLA to tackle the problem. POLA is a personalized one-shot and two-step FL method capable of generating high-performance personalized models for each independent participant. The proposed method, POLA was compared with two other PFL methods in experiments, and the results indicate that it not only effectively improves the prediction performance of FL but also significantly reduces the communication rounds. Moreover, its generality and extensibility also make it potential to be extended to other similar cross-silo FL application scenarios. |
| first_indexed | 2025-11-15T14:03:27Z |
| format | Article |
| id | upm-109394 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T14:03:27Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1093942024-08-05T02:40:30Z http://psasir.upm.edu.my/id/eprint/109394/ Personalized federated learning for in-hospital mortality prediction of multi-center ICU Deng, Ting Hamdan, Hazlina Yaakob, Razali Kasmiran, Khairul Azhar Federated learning (FL), as a paradigm for addressing challenges of machine learning (ML) to be applied in private distributed data provides a novel and promising scheme to promote ML in multiple independently distributed healthcare institutions. However, the non-IID and unbalanced nature of the data distribution can decrease its performance, even resulting in the institutions losing motivation to participate in its training. This paper explored the problem with an in-hospital mortality prediction task under an actual multi-center ICU electronic health record database that preserves the original non-IID and unbalanced data distribution. It first analyzed the reason for the performance degradation of baseline FL under this data scenario, and then proposed a personalized FL (PFL) approach named POLA to tackle the problem. POLA is a personalized one-shot and two-step FL method capable of generating high-performance personalized models for each independent participant. The proposed method, POLA was compared with two other PFL methods in experiments, and the results indicate that it not only effectively improves the prediction performance of FL but also significantly reduces the communication rounds. Moreover, its generality and extensibility also make it potential to be extended to other similar cross-silo FL application scenarios. Institute of Electrical and Electronics Engineers 2023-02-01 Article PeerReviewed Deng, Ting and Hamdan, Hazlina and Yaakob, Razali and Kasmiran, Khairul Azhar (2023) Personalized federated learning for in-hospital mortality prediction of multi-center ICU. IEEE Access, 11. 11652- 11663. ISSN 2169-3536 https://ieeexplore.ieee.org/document/10034741 10.1109/access.2023.3241488 |
| spellingShingle | Deng, Ting Hamdan, Hazlina Yaakob, Razali Kasmiran, Khairul Azhar Personalized federated learning for in-hospital mortality prediction of multi-center ICU |
| title | Personalized federated learning for in-hospital mortality prediction of multi-center ICU |
| title_full | Personalized federated learning for in-hospital mortality prediction of multi-center ICU |
| title_fullStr | Personalized federated learning for in-hospital mortality prediction of multi-center ICU |
| title_full_unstemmed | Personalized federated learning for in-hospital mortality prediction of multi-center ICU |
| title_short | Personalized federated learning for in-hospital mortality prediction of multi-center ICU |
| title_sort | personalized federated learning for in-hospital mortality prediction of multi-center icu |
| url | http://psasir.upm.edu.my/id/eprint/109394/ http://psasir.upm.edu.my/id/eprint/109394/ http://psasir.upm.edu.my/id/eprint/109394/ |