Reference : Fairness, integrity, and privacy in a scalable blockchain-based federated learning system
Scientific journals : Article
Engineering, computing & technology : Computer science
Business & economic sciences : Management information systems
Security, Reliability and Trust
Fairness, integrity, and privacy in a scalable blockchain-based federated learning system
Rückel, Timon []
Sedlmeir, Johannes mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX >]
Hofmann, Peter []
Computer Networks
[en] Blockchain ; differential privacy ; distributed ledger technology ; federated machine learning ; zero-knowledge proofs
[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.

File(s) associated to this reference

Fulltext file(s):

Limited access
1-s2.0-S1389128621005132-main.pdfPublisher postprint1.24 MBRequest a copy
Open access
2111.06290.pdfAuthor preprint897.59 kBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.