Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System

TL;DR

This paper proposes a scalable blockchain-based federated learning system that integrates differential privacy and zero-knowledge proofs to enhance fairness, integrity, and privacy for all clients.

Contribution

It introduces a novel federated learning architecture combining blockchain, differential privacy, and zero-knowledge proofs to address fairness, integrity, and privacy challenges.

Findings

Successful implementation of a proof-of-concept with linear regression

Demonstrates alignment of economic incentives with trust and confidentiality

Shows scalability and transparency of the proposed system

Abstract

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.