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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.17228 |
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| _version_ | 1866909266919555072 |
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| author | Damiani, Celeste Rodina, Yulia Decherchi, Sergio |
| author_facet | Damiani, Celeste Rodina, Yulia Decherchi, Sergio |
| contents | Federated learning is becoming an increasingly viable and accepted strategy for building machine learning models in critical privacy-preserving scenarios such as clinical settings. Often, the data involved is not limited to clinical data but also includes additional omics features (e.g. proteomics). Consequently, data is distributed not only across hospitals but also across omics centers, which are labs capable of generating such additional features from biosamples. This scenario leads to a hybrid setting where data is scattered both in terms of samples and features. In this hybrid setting, we present an efficient reformulation of the Kernel Regularized Least Squares algorithm, introduce two variants and validate them using well-established datasets. Lastly, we discuss security measures to defend against possible attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17228 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Hybrid Federated Kernel Regularized Least Squares Algorithm Damiani, Celeste Rodina, Yulia Decherchi, Sergio Machine Learning Federated learning is becoming an increasingly viable and accepted strategy for building machine learning models in critical privacy-preserving scenarios such as clinical settings. Often, the data involved is not limited to clinical data but also includes additional omics features (e.g. proteomics). Consequently, data is distributed not only across hospitals but also across omics centers, which are labs capable of generating such additional features from biosamples. This scenario leads to a hybrid setting where data is scattered both in terms of samples and features. In this hybrid setting, we present an efficient reformulation of the Kernel Regularized Least Squares algorithm, introduce two variants and validate them using well-established datasets. Lastly, we discuss security measures to defend against possible attacks. |
| title | A Hybrid Federated Kernel Regularized Least Squares Algorithm |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.17228 |