DSFL: a blockchain-based data sharing and federated learning framework

DSFL: a blockchain-based data sharing and federated learning framework

연구 분야: Verification

학회: Applied Intelligence

The massive amount of data generated by the proliferation of Internet of Things (IoT) devices has become one of the key factors driving the advancement of artificial intelligence (AI) technology. However, the lack of storage space and limited computational power of edge devices make it difficult to directly process large data volumes or run complex machine learning algorithms on these devices. At the same time, existing Federated Learning (FL) schemes still face a number of shortcomings, including a single point of failure, vulnerability to poisoning attacks, and a lack of incentives. To address the above issues, we propose DSFL, a blockchain-based framework for fair data sharing and FL. Specifically, we combine digital envelope technology and one-way accumulator with smart contracts to design fair, secure, and trustworthy data sharing protocols that facilitate edge devices to share data proactively, realize the value of data and reduce storage pressure. In addition, we propose blockchain extension schemes suitable for coupling with FL to improve training efficiency. Importantly, the node management mechanism and incentive algorithms are designed to effectively monitor and trace the behavior of nodes, and promote the virtuous cycle of model training and the motivation of participants. Experimental results show that DSFL is able to ensure fair data sharing and efficient model training without the involvement of trusted third parties. In particular, it is able to achieve model accuracy close to that of existing popular schemes even when 40% of the nodes are lazy, providing an excellent defense against malicious nodes.