mlsanityData to 14 Oct 2026

Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunities

arXiv 2503.09649 ↗First posted 12 Mar 2025q-bio.OT cs.LG stat.MLcited by 2

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Abstract (arXiv)

Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy. This paper provides a gentle introduction to this approach in bioinformatics, and is the first to review key applications in proteomics, genome-wide association studies (GWAS), single-cell and multi-omics studies in their legal as well as methodological and infrastructural challenges. As the evolution of biobanks in genetics and systems biology has proved, accessing more extensive and varied data pools leads to a faster and more robust exploration and translation of results. More widespread use of federated learning may have a similar impact in bioinformatics, allowing academic and clinical institutions to access many combinations of genotypic, phenotypic and environmental information that are undercovered or not included in existing biobanks.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 34 of its 123 references that are in the map. First posted on arXiv 12 Mar 2025.

Builds on

34 of 123 references.

References · 34

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Deep Learning with Differential Privacycited by 2,728
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  4. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  5. 2020The Future of Digital Health with Federated Learningcited by 633
  6. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  7. 2021Towards Personalized Federated Learningcited by 464
  8. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  9. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 321
  10. 2019Differential Privacy Has Disparate Impact on Model Accuracycited by 316
  11. 2018Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learningcited by 225
  12. 2021Federated Learning for Open Bankingcited by 141
22 more
  1. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 138
  2. 2022Reconstructing Training Data from Trained Neural Networkscited by 133
  3. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  4. 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 84
  5. 2021HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Imagescited by 64
  6. 2020Data Poisoning Attacks on Federated Machine Learningcited by 62
  7. 2020Federated Learning for Breast Density Classification: A Real-World Implementationcited by 62
  8. 2021OpenFL: An open-source framework for Federated Learningcited by 58
  9. 2018Privacy-preserving Predictioncited by 48
  10. 2022Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imagingcited by 39
  11. 2022Cross-Silo Federated Learning: Challenges and Opportunitiescited by 38
  12. 2020WAFFLE: Watermarking in Federated Learningcited by 37
  13. 2024Advances and Open Challenges in Federated Foundation Modelscited by 36
  14. 2020Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japancited by 23
  15. 2021Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Diseasecited by 19
  16. 2022Secure Aggregation for Federated Learning in Flowercited by 15
  17. 2024Federated Learning in Practice: Reflections and Projectionscited by 15
  18. 2018Locally Private Bayesian Inference for Count Modelscited by 9
  19. 2021Federated Learning Attacks Revisited: A Critical Discussion of Gaps, Assumptions, and Evaluation Setupscited by 5
  20. 2021Semi-Supervised Federated Peer Learning for Skin Lesion Classificationcited by 4
  21. 2021Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sourcescited by 4
  22. 2021HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learningcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Securing Genomic Data Against Inference Attacks in Federated Learning Environmentscited by 1
  2. 2026FPLIER: Federated Pathway-Level Information Extractorcited by 0