Article (Scientific journals)
Fairness, integrity, and privacy in a scalable blockchain-based federated learning system
Rückel, Timon; SEDLMEIR, Johannes; Hofmann, Peter
2022In Computer Networks, 202
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Keywords :
Blockchain; differential privacy; distributed ledger technology; federated machine learning; zero-knowledge proofs
Abstract :
[en] 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 regressions 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.
Disciplines :
Management information systems
Computer science
Author, co-author :
Rückel, Timon
SEDLMEIR, Johannes  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX
Hofmann, Peter
External co-authors :
yes
Language :
English
Title :
Fairness, integrity, and privacy in a scalable blockchain-based federated learning system
Publication date :
15 January 2022
Journal title :
Computer Networks
ISSN :
1389-1286
eISSN :
1872-7069
Publisher :
Elsevier, Amsterdam, Netherlands
Volume :
202
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Security, Reliability and Trust
Available on ORBilu :
since 13 January 2023

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