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

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.

Automated summary

Research shows that the lack of broad adoption of federated machine learning in practice is mainly due to the significant challenge of simultaneously achieving fairness, integrity, and privacy preservation. To address this issue, a FL system that incorporates blockchain technology, local differential privacy, and zero-knowledge proofs is proposed.