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

[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 :

Gestion des systèmes d’information
Sciences informatiques

Auteur, co-auteur :

Rückel, Timon

SEDLMEIR, Johannes ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX

Hofmann, Peter

Co-auteurs externes :

yes

Langue du document :

Anglais

Titre :

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

Date de publication/diffusion :

15 janvier 2022

Titre du périodique :

Computer Networks

ISSN :

1389-1286

eISSN :

1872-7069

Maison d'édition :

Elsevier, Amsterdam, Pays-Bas

Volume/Tome :

202

Peer reviewed :

Peer reviewed vérifié par ORBi

Focus Area :

Security, Reliability and Trust

Disponible sur ORBilu :

depuis le 13 janvier 2023

Statistiques

Nombre de vues

230 (dont 3 Unilu)

Nombre de téléchargements

135 (dont 4 Unilu)

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citations Scopus^®
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