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

Blockchained Trustable Federated Learning Utilizing Voting Accountability for Malicious Actor Mitigation

“…Reference [33] introduced the incorporation of local differential privacy and zeroknowledge proof in a blockchain-based FL framework. While this work provides confidentiality using differential privacy and incentives, it does not provide a trust management system to combat malicious actors masquerading as honest workers.…”

“…Some aspects of importance that previous works have yet to highlight are integrity and authentication. Blockchain has been used as a method to provide incentive systems and FL reliability (e.g., SPoF) but prior studies have not highlighted the immutability of the blockchain as a key feature [5][6][7][8]12,13,16,33]. Leveraging the blockchain as a verification mechanism can provide a way to ensure integrity of the data transfer process in federated learning.…”