Federated Learning Architectures for Predicting Adverse Drug Events in Oncology Without Compromising Patient Privacy

Salvation Ifechukwude Atalor

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1708491PublishedVol 2 · Issue 12

Federated Learning Architectures for Predicting Adverse Drug Events in Oncology Without Compromising Patient Privacy

Salvation Ifechukwude Atalor

Subject area: Science,Engineering and Technology · Area of research: Computer Science

Abstract

The exponential growth of biomedical data in oncology presents immense opportunities for leveraging machine learning to predict adverse drug events (ADEs) and improve patient outcomes. However, traditional centralized data collection methods pose significant privacy, regulatory, and interoperability challenges, particularly when aggregating sensitive health information across multiple institutions. This review investigates the role of federated learning (FL) architectures as a transformative solution for ADE prediction in oncology without compromising patient privacy. FL enables collaborative model training across distributed datasets by exchanging model parameters rather than raw data, thus preserving data sovereignty while ensuring analytical rigor. We explore diverse data sources integral to FL in oncology, including electronic health records (EHRs), genomic sequences, and clinical trial data, and assess their integration through privacy-preserving protocols such as differential privacy and secure multiparty computation. Furthermore, the study evaluates existing FL frameworks and proposes scalable architectural designs optimized for heterogeneous, multi-modal oncology datasets. We discuss critical challenges including model heterogeneity, communication efficiency, and real-time personalization, alongside strategies for enhancing robustness and generalizability of FL models across diverse clinical environments. Finally, we assess the translational potential of FL systems, emphasizing regulatory readiness, clinical validation, and deployment pathways in real-world oncology settings. Through a rigorous synthesis of current methodologies and forward-looking frameworks, this review underscores FL's pivotal role in advancing precision oncology and fostering a secure, collaborative ecosystem for ADE prediction and therapeutic decision support.

Keywords

Federated Learning, Adverse Drug Events, Oncology, Patient Privacy, Precision Medicine, Decentralized Machine Learning

How to cite this paper

Salvation Ifechukwude Atalor "Federated Learning Architectures for Predicting Adverse Drug Events in Oncology Without Compromising Patient Privacy" Iconic Research And Engineering Journals Volume 2 Issue 12 2019 Page 246-269

Salvation Ifechukwude Atalor "Federated Learning Architectures for Predicting Adverse Drug Events in Oncology Without Compromising Patient Privacy" Iconic Research And Engineering Journals, vol. 2, no. 12, Jun. 2019

Salvation Ifechukwude Atalor (2019). Federated Learning Architectures for Predicting Adverse Drug Events in Oncology Without Compromising Patient Privacy. Iconic Research And Engineering Journals, 2(12).

Salvation Ifechukwude Atalor "Federated Learning Architectures for Predicting Adverse Drug Events in Oncology Without Compromising Patient Privacy" Iconic Research And Engineering Journals, vol. 2, no. 12, Jun. 2019.

@article{1708491,
      author = {Salvation Ifechukwude Atalor},
      title = {Federated Learning Architectures for Predicting Adverse Drug Events in Oncology Without Compromising Patient Privacy},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {2},
      number = {12},
      pages = {246-269},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708491.pdf},
      abstract = {The exponential growth of biomedical data in oncology presents immense opportunities for leveraging machine learning to predict adverse drug events (ADEs) and improve patient outcomes. However, traditional centralized data collection methods pose significant privacy, regulatory, and interoperability challenges, particularly when aggregating sensitive health information across multiple institutions. This review investigates the role of federated learning (FL) architectures as a transformative solution for ADE prediction in oncology without compromising patient privacy. FL enables collaborative model training across distributed datasets by exchanging model parameters rather than raw data, thus preserving data sovereignty while ensuring analytical rigor. We explore diverse data sources integral to FL in oncology, including electronic health records (EHRs), genomic sequences, and clinical trial data, and assess their integration through privacy-preserving protocols such as differential privacy and secure multiparty computation. Furthermore, the study evaluates existing FL frameworks and proposes scalable architectural designs optimized for heterogeneous, multi-modal oncology datasets. We discuss critical challenges including model heterogeneity, communication efficiency, and real-time personalization, alongside strategies for enhancing robustness and generalizability of FL models across diverse clinical environments. Finally, we assess the translational potential of FL systems, emphasizing regulatory readiness, clinical validation, and deployment pathways in real-world oncology settings. Through a rigorous synthesis of current methodologies and forward-looking frameworks, this review underscores FL's pivotal role in advancing precision oncology and fostering a secure, collaborative ecosystem for ADE prediction and therapeutic decision support.},
      keywords = {Federated Learning, Adverse Drug Events, Oncology, Patient Privacy, Precision Medicine, Decentralized Machine Learning},
      month = {June},
  }