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Fairness, integrity, and privacy in a scalable blockchain-based federated learning system

Title data

Rückel, Timon ; Sedlmeir, Johannes ; Hofmann, Peter:
Fairness, integrity, and privacy in a scalable blockchain-based federated learning system.
In: Computer Networks. Vol. 202 (2022) .
ISSN 1872-7069
DOI: https://doi.org/10.1016/j.comnet.2021.108621

Project information

Project title:
Project's official titleProject's id
Projektgruppe WI Künstliche IntelligenzNo information
Projektgruppe WI BLockchain-LaborNo information

Abstract in another language

Federated machine learning (FL) allows to collectively train models on sensitive data as only the clients’ models and not their training data need to be shared. However, despite the attention that research on FL has drawn, the concept still lacks broad adoption in practice. One of the key reasons is the great challenge to implement FL systems that simultaneously achieve fairness, integrity, and privacy preservation for all participating clients. To contribute to solving this issue, our paper suggests a FL system that incorporates blockchain technology, local differential privacy, and zero-knowledge proofs. Our implementation of a proof-of-concept with multiple linear regression illustrates that these state-of-the-art technologies can be combined to a FL system that aligns economic incentives, trust, and confidentiality requirements in a scalable and transparent system.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Blockchain; Differential privacy; Distributed ledger technology; Federated machine learning; Zero-knowledge proof
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Faculties
Faculties > Faculty of Law, Business and Economics
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 06 Dec 2021 08:57
Last Modified: 22 Dec 2021 14:01
URI: https://eref.uni-bayreuth.de/id/eprint/68077