Abstract
While medical data is integral to building robust predictive machine learning models for medical research, obtaining access to medical data is increasingly difficult. The challenges primarily arise from obtaining consent, concerns around the privacy and security of medical data and the technical challenge of migrating what can be huge datasets to a centralised location. As a result, this chapter analyses the question, “How can we make medical data more accessible for medical research whilst addressing the ethical and technical issues around data privacy and data-sharing?” Moreover, this work expands on federated learning that represents a paradigm shift in machine learning from both a technical and sociological perspective. From a technical perspective, federated learning enables machine learning models to be trained in a decentralised manner. It thus allows researchers to utilise data stored in separate locations. From a sociological perspective, federated learning represents a shift in the power dynamic between those providing and those using medical data for research. Under a federated learning framework, raw data never leaves the client’s device. Instead, the centralised server only receives encrypted parameter updates after a shared model is sent and trained locally on each client device. This ensures that the entities that provide medical data have more control over where their data is stored and what information is shared with other parties. Even though federated algorithms have a slightly lower accuracy when compared to non-federated algorithms, it comes with data privacy benefits that non-federated algorithms cannot provide.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
K. Abouelmehdi, A. Beni-Hessane, H. Khaloufi, Big healthcare data: preserving security and privacy. J. Big Data 5(1), 1–18 (2018)
A. Ballantyne, How should we think about clinical data ownership? J. Med. Ethics 46(5), 289–294 (2020)
R. Bey et al., Fold-stratified cross-validation for unbiased and privacy-preserving federated learning. J. Am. Med. Inf. Assoc. 27(8), 1244–1251 (2020)
S. Boughorbel et al., Federated uncertainty-aware learning for distributed hospital ehr data (2019). arXiv preprint arXiv:1910.12191
A. Bourke, G. Bourke, Who owns patient data? the answer is not that simple (2020). https://blogs.bmj.com/bmj/2020/08/06/who-owns-patient-data-the-answer-is-not-that-simple/
Y. Chen et al., Fedhealth: A federated transfer learning framework for wearable healthcare. IEEE Intell. Syst. 35(4), 83–93 (2020)
O. Choudhury, A. Gkoulalas-Divanis et al, Anonymizing data for privacy-preserving federated learning (2020). arXiv:2002.09096
O. Choudhury, Y. Park et al., Predicting adverse drug reactions on distributed health data using federated learning, in AMIA Annual Symposium Proceedings, vol. 2019. (American Medical Informatics Association, 2019), p. 313
J. Cui et al., FeARH: Federated machine learning with anonymous random hybridization on electronic medical records. J. Biomed. Inf. 117, 103735 (2021)
S. Dash et al., Big data in healthcare: management, analysis and future prospects. J. Big Data 6(1), 1–25 (2019)
S. Day, M. Zweig, Beyond wellness for the healthy: digital health consumer adoption (2018). https://rockhealth.com/reports/beyond-wellness-for-the-healthy-digital-health-consumer-adoption-2018/
D.-L. Donnelly, Privacy by design’ in the Eu general data protection regulation: a new privacy standard or the emperor’s new clothes? S. Afr. Law J. 139(3), 541–576 (2022)
E.S. Dove, M. Phillips, Privacy law, data sharing policies, and medical data: a comparative perspective, in Medical Data Privacy Handbook, pp. 639–678
Fallah, A., Mokhtari, A., Ozdaglar, A., Personalized federated learning with theoretical guarantees: a model agnostic meta-learning approach. Adv. Neural Inf. Process. Syst. 33, 3557–3568 (2020)
D. Gao et al., Hhhfl: Hierarchical heterogeneous horizontal federated learning for electroencephalography (2019). arXiv preprint arXiv:1909.05784
A. Holst, Amount of data created, consumed, and stored 2010–2025. Technol. Telecommun. Retrieved, 06–29 (2021)
L. Huang, A. L. Shea et al., Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. J Biomed. Inf. 99, 103291 (2019)
L. Huang, Y. Yifeng et al., LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data. Plos One 15(4), e0230706 (2020)
C. Ju, D. Gao et al., Federated transfer learning for EEG signal classification, in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (IEEE, 2020), pp. 3040–3045
C. Ju, R. Zhao et al., Privacy-preserving technology to help millions of people: federated prediction model for stroke prevention (2020). arXiv preprint arXiv:2006.10517
M. Lawler, T. Maughan, From Rosalind Franklin to Barack Obama: data sharing challenges and solutions in genomics and personalised medicine. New Bioeth. 23(1), 64–73 (2017)
T. Li, A.K. Sahu et al., Federated optimization in heterogeneous networks, in Proceedings of Machine Learning and Systems, vol. 2, ed. by I. Dhillon, D. Papailiopoulos, V. Sze, pp. 429–450 (2020a). https://proceedings.mlsys.org/paper/2020/file/38af86134b65d0f10fe33d30dd76442e-Paper.pdf
T. Li, A.K. Sahu et al., Federated optimization in heterogeneous networks. Proceed. Mach. Learn. Syst. 2, 429–450 (2020b)
T. Li, M. Sanjabi et al., Fair resource allocation in federated learning (2019). arXiv preprint arXiv:1905.10497
X. Li et al., Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results. Med. Image Anal. 65, 101765 (2020)
D. Liu et al., FADL: Federated-autonomous deep learning for distributed electronic health record (2018). arXiv preprint arXiv:1811.11400
Y. Lu et al., Differentially private asynchronous federated learning for mobile edge computing in urban informatics. IEEE Trans. Ind. Inf. 16(3), 2134–2143 (2019)
B. McMahan et al., Communication-efficient learning of deep networks from decentralized data. Artif. Intell. Stati. PMLR 1273–1282 (2017)
M. Mirchev, I. Mircheva, A. Kerekovska, The academic viewpoint on patient data ownership in the context of big data: scoping review. J. Med. Internet Res. 22(8), e22214 (2020)
M. Mostert et al., Big Data in medical research and EU data protection law: challenges to the consent or anonymise approach. Eur. J. Hum. Genet. 24(7), 956–960 (2016)
J. Passerat-Palmbach et al., A blockchain-orchestrated federated learning architecture for healthcare consortia (2019). arXiv preprint arXiv:1910.12603
S.R. Pfohl, A.M. Dai, K. Heller, Federated and differentially private learning for electronic health records (2019). arXiv preprint arXiv:1911.05861
N. Rieke et al., The future of digital health with federated learning. NPJ Digit. Med. 3(1), 119 (2022)
A. Sadilek et al., Privacy-first health research with federated learning. NPJ Digit. Med. 4(1), 132 (2021)
Series, Cisco Cybersecurity (2020). Consumer Privacy Survey (Cisco, 2019)
N. Sethi, G.T. Laurie, Delivering proportionate governance in the era of eHealth: making linkage and privacy work together. Med. Law Int. 13(2–3), 168–204 (2013)
R. Shao et al., Stochastic channel-based federated learning for medical data privacy preserving (2019). arXiv preprint. arXiv:1910.11160
P. Sharma, F.E. Shamout, D.A. Clifton, Preserving patient privacy while training a predictive model of in-hospital mortality (2019). arXiv preprint arXiv:1912.00354
M. Sheller J et al., Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation, in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018 (Springer, Granada, Spain, 2019). Revised Selected Papers, Part I 4., pp. 92–104
L. Siemons et al., Big Data for personalized healthcare. Int. J. Adv. Syst. Meas. 9(3/4), 220–229 (2016)
S.S. Silva Rincon, Complex anatomical patterns in mild cognitive impairment to Alzheimer’s disease/conversion (n.d.)
V. Smith et al., Federated multi-task learning. Adv. Neural Inf. Process. Syst. 30 (2019)
B. Van Asbroeck, J. Debussche, J. C’sar, Big data & issues & opportunities: data ownership. Bird & Bird (2019)
W.G. Van Panhuis et al., A systematic review of barriers to data sharing in public health. BMC Public Health 14(1), 1–9 (2014)
C. Wu et al., Communication-efficient federated learning via knowledge distillation. Nat. Commun. 13(1), 2032 (2022)
B. Yuan, S. Ge, W. Xing, A federated learning framework for healthcare IoT devices (2020). arXiv preprint arXiv:2005.05083
H. Zhang et al., FedPCC: parallelism of communication and computation for federated learning in wireless networks, in IEEE Transactions on Emerging Topics in Computational Intelligence 6.6, pp. 1368–1377. https://doi.org/10.1109/TETCI.2022.3170471.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Chen, J., Farid, F., Polash, M. (2023). Federated Learning: An Alternative Approach to Improving Medical Data Privacy and Security. In: Daimi, K., Alsadoon, A., Seabra Dos Reis, S. (eds) Current and Future Trends in Health and Medical Informatics. Studies in Computational Intelligence, vol 1112. Springer, Cham. https://doi.org/10.1007/978-3-031-42112-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-42112-9_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-42111-2
Online ISBN: 978-3-031-42112-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)