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) . - 108621.
ISSN 1872-7069
DOI: https://doi.org/10.1016/j.comnet.2021.108621

Project 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.