Transparent and Accountable Training Data Sharing in Decentralized Machine Learning Systems

Abstract

In Decentralized Machine Learning (DML) systems, system participants contribute their resources to assist others in developing machine learning solutions. Identifying malicious contributions in DML systems is challenging, which has led to the exploration of blockchain technology. Blockchain leverages its transparency and immutability to record the provenance and reliability of training data. However, storing massive datasets or implementing model evaluation processes on smart contracts incurs high computational costs. Additionally, current research on preventing malicious contributions in DML systems primarily focuses on protecting models from being exploited by workers who contribute incorrect or misleading data. However, less attention has been paid to the scenario where malicious requesters intentionally manipulate test data during evaluation to gain an unfair advantage. This paper proposes a transparent and accountable training data sharing method that securely shares data among potentially malicious system participants. First, we introduce a blockchain-based DML system architecture that supports secure training data sharing through the IPFS network. Second, we design a blockchain smart contract to transparently split training datasets into training and test datasets, respectively, without involving system participants. Under the system, transparent and accountable training data sharing can be achieved with attribute-based proxy re-encryption. We demonstrate the security analysis for the system, and conduct experiments on the Ethereum and IPFS platforms to show the feasibility and practicality of the system.