Blockchain-orchestrated machine learning for privacy preserving federated learning in electronic health data
Abstract
Machine learning and blockchain technology have been explored for potential applications in medicine with only modest success to date. Focus has shifted to exploring the intersection of these technologies along with other privacy preserving encryption techniques for better utility. This combination applied to federated learning, which allows remote execution of function and analysis without the need to move highly regulated personal health information, seems to be the key to successful applications of these technologies to rapidly advance evidence-based medicine. We give a brief history of these technologies in medicine, outlining some of the challenges with successful use. We then explore a more detailed combination of usage with an emphasis on decentralizing or federating the learning process along with auditability and incentivization blockchain can allow in the machine learning process. Based on the cost-benefit analysis of previous efforts, we provide the framework for an advanced blockchain-orchestrated machine learning system for privacy preserving federated learning in medicine and a new utility in health. Six critical elements for this approach in the future will be:(a) Data and analytic processes discoverable on secure public blockchain while retaining privacy of the data and analytic processes(b) Value fabricated by generating data/compute matches that were previously illegal, unethical and infeasible(c) Compute guarantees provided by federated learning and advanced cryptography(d) Privacy guarantees provided by software (e.g., Homomorphic Encryption, Secure Multi-Party Computation, ...) and hardware (e.g., Intel SGX and AMD SEV-SNP) cryptography(e) Data quality incentivized via tokenized reputation-based rewards(f) Discarding of poor data accomplished via model poisoning attack prevention techniques.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Passerat-Palmbach et al. proposed an FL-chain framework with six elements for privacy-preserving in electronic health data to avoid bias for higher security and persistence.
“Passerat-Palmbach et al. [43] proposed an FL-chain framework with six elements for privacy-preserving in electronic health data to avoid bias for higher security and persistence.”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [43]
All four FL-chain systems [42,43,44,45] from this review adopted additional privacy enhancement methods to improve privacy protection.
“All of the four FL-chain systems [42,43,44,45] from this review adopted additional privacy enhancement methods to improve privacy protection.”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [43]
The consensus process in the FL-chain systems [42,43,44,45] requires significant computational resources.
“Besides the integration of the additional privacy enhancement methods, the consensus process in the FL-chain systems [42,43,44,45] requires significant computational resources.”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [43]
Four FL-chain healthcare applications in this review demonstrate that blockchain technology is relevant to FL.
“Four FL-chain healthcare applications [42,43,44,45] in this review prove that the blockchain technology is relevant to FL.”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [43]
The existing work in this review presented approaches including adding gradient [42], extra elements [43], hashed EHR address [44], and additional rules [45].
“The existing work in this review presented the approaches including adding gradient [42], extra elements [43], hashed EHR address [44], and additional rules [45].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [43]
Integrating blockchain into federated settings decentralizes FL and allows elimination of a single central server in model aggregation.
“Particularly, the integration of blockchain into federated settings decentralizes the FL that allows for the elimination of a single central server in the model aggregation [54].”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [54]
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Federated learning for smart healthcare: A survey
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A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems
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- Authors
- Passerat-Palmbach, J., Farnan, T., McCoy, M., Harris, J. D., Manion, S. T., Flannery, H. L., & Gleim, B.
- Affiliations
- University of California San Diego (US)
- Thomas Jefferson University (US)
- Microsoft (United States) (US)
- ConsenSys (United States) (US)
- ORCID
- Manion, Sean T.
- Venue
- 2020 IEEE International Conference on Blockchain (Blockchain)
- Publisher
- IEEE
- Published
- 2020-11
- Pages
- 550-555
- OpenAlex
- W3113052065
- Cited by
- 84
- References
- 26
- Citations by year
- 2026: 3 · 2025: 17 · 2024: 23 · 2023: 27 · 2022: 4 · 2021: 10
- FWCI
- 7.77
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Blockchain (Blockchain) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Keywords
- blockchain, federated learning, privacy-preserving machine learning, electronic health data, homomorphic encryption, secure multi-party computation, Intel SGX, AMD SEV-SNP, model poisoning, auditability, decentralized learning
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