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 :

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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