Target informed client recruitment for efficient federated learning in healthcare
Abstract Background Modern machine learning and deep learning methods have been widely incorporated in decision making processes in healthcare in the form of decision support mechanisms. In healthcare, data are abundant but typically not centrally available and, therefore, require some form of aggre...
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| Format: | Article |
| Language: | English |
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BMC
2024-12-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-024-02798-4 |
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| author | Vincent Scheltjens Lyse Naomi Wamba Momo Wouter Verbeke Bart De Moor |
| author_facet | Vincent Scheltjens Lyse Naomi Wamba Momo Wouter Verbeke Bart De Moor |
| author_sort | Vincent Scheltjens |
| collection | DOAJ |
| description | Abstract Background Modern machine learning and deep learning methods have been widely incorporated in decision making processes in healthcare in the form of decision support mechanisms. In healthcare, data are abundant but typically not centrally available and, therefore, require some form of aggregation to facilitate training procedures. Aggregating sensitive data poses a significant privacy risk, which is why, both in Europe and the United States, legal frameworks regulate the treatment of such data. Whilst these measures protect the individual behind the data, they pose a significant challenge that results in extensive legal administration related to data sharing efforts. Federated learning (FL) offers a way to mitigate these challenges by allowing to learn models in distributed fashion, eliminating the need to aggregate data for the purpose of training. However, FL comes with a new set of challenges related to communication overhead, client selection and efficiency of the FL training procedure, among others. Methods In this work, we extend on a previously proposed client recruitment approach by incorporating knowledge on the local hardware such that it becomes possible to recruit a subset of clients for the federation based on the construct of client-level representativeness, which is expressed in terms of the local target distribution divergence, sample size, and the underlying hardware. Results We show that, for prominent, medical regression and classification tasks, the recruitment approach yields results that are on par, or better, compared to the central and federated approaches. The proposed approach requires a mere fraction of the data for training and reduces the training time by a factor of 3-4. In addition, we show that excluded clients can still significantly benefit from the resulting federated model through local fine-tuning. Conclusions By expressing the representativeness of clients in function of the deviation in the local target distribution, the sample size and efficiency of the underlying hardware, we are able to define a recruitment approach that yields a subset of clients for the federation resulting in significantly reduced training time, without harming predictive performance, whilst improving the privacy preserving characteristics compared to the standard FL and central approaches. |
| format | Article |
| id | doaj-art-c7142d787951435f9ab3ae15f3d0131b |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-c7142d787951435f9ab3ae15f3d0131b2024-12-22T12:30:00ZengBMCBMC Medical Informatics and Decision Making1472-69472024-12-0124112210.1186/s12911-024-02798-4Target informed client recruitment for efficient federated learning in healthcareVincent Scheltjens0Lyse Naomi Wamba Momo1Wouter Verbeke2Bart De Moor3Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU LeuvenDepartment of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU LeuvenFaculty of Economics and Business, KU LeuvenDepartment of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU LeuvenAbstract Background Modern machine learning and deep learning methods have been widely incorporated in decision making processes in healthcare in the form of decision support mechanisms. In healthcare, data are abundant but typically not centrally available and, therefore, require some form of aggregation to facilitate training procedures. Aggregating sensitive data poses a significant privacy risk, which is why, both in Europe and the United States, legal frameworks regulate the treatment of such data. Whilst these measures protect the individual behind the data, they pose a significant challenge that results in extensive legal administration related to data sharing efforts. Federated learning (FL) offers a way to mitigate these challenges by allowing to learn models in distributed fashion, eliminating the need to aggregate data for the purpose of training. However, FL comes with a new set of challenges related to communication overhead, client selection and efficiency of the FL training procedure, among others. Methods In this work, we extend on a previously proposed client recruitment approach by incorporating knowledge on the local hardware such that it becomes possible to recruit a subset of clients for the federation based on the construct of client-level representativeness, which is expressed in terms of the local target distribution divergence, sample size, and the underlying hardware. Results We show that, for prominent, medical regression and classification tasks, the recruitment approach yields results that are on par, or better, compared to the central and federated approaches. The proposed approach requires a mere fraction of the data for training and reduces the training time by a factor of 3-4. In addition, we show that excluded clients can still significantly benefit from the resulting federated model through local fine-tuning. Conclusions By expressing the representativeness of clients in function of the deviation in the local target distribution, the sample size and efficiency of the underlying hardware, we are able to define a recruitment approach that yields a subset of clients for the federation resulting in significantly reduced training time, without harming predictive performance, whilst improving the privacy preserving characteristics compared to the standard FL and central approaches.https://doi.org/10.1186/s12911-024-02798-4Federated learningClient recruitmentDeep learning |
| spellingShingle | Vincent Scheltjens Lyse Naomi Wamba Momo Wouter Verbeke Bart De Moor Target informed client recruitment for efficient federated learning in healthcare BMC Medical Informatics and Decision Making Federated learning Client recruitment Deep learning |
| title | Target informed client recruitment for efficient federated learning in healthcare |
| title_full | Target informed client recruitment for efficient federated learning in healthcare |
| title_fullStr | Target informed client recruitment for efficient federated learning in healthcare |
| title_full_unstemmed | Target informed client recruitment for efficient federated learning in healthcare |
| title_short | Target informed client recruitment for efficient federated learning in healthcare |
| title_sort | target informed client recruitment for efficient federated learning in healthcare |
| topic | Federated learning Client recruitment Deep learning |
| url | https://doi.org/10.1186/s12911-024-02798-4 |
| work_keys_str_mv | AT vincentscheltjens targetinformedclientrecruitmentforefficientfederatedlearninginhealthcare AT lysenaomiwambamomo targetinformedclientrecruitmentforefficientfederatedlearninginhealthcare AT wouterverbeke targetinformedclientrecruitmentforefficientfederatedlearninginhealthcare AT bartdemoor targetinformedclientrecruitmentforefficientfederatedlearninginhealthcare |