Toward Trustworthy AI: Blockchain-Based Architecture Design for Accountability and Fairness of Federated Learning Systems

【Author】 Lo, Sin Kit; Liu, Yue; Lu, Qinghua; Wang, Chen; Xu, Xiwei; Paik, Hye-Young; Zhu, Liming

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】Federated learning is an emerging privacy-preserving AI technique where clients (i.e., organizations or devices) train models locally and formulate a global model based on the local model updates without transferring local data externally. However, federated learning systems struggle to achieve trustworthiness and embody responsible AI principles. In particular, federated learning systems face accountability and fairness challenges due to multistakeholder involvement and heterogeneity in client data distribution. To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture. We first design a smart contract-based data-model provenance registry to enable accountability. Additionally, we propose a weighted fair data sampler algorithm to enhance fairness in training data. We evaluate the proposed approach using a COVID-19 X-ray detection use case. The evaluation results show that the approach is feasible to enable accountability and improve fairness. The proposed algorithm can achieve better performance than the default federated learning setting in terms of the model's generalization and accuracy.

【Keywords】Data models; Collaborative work; Training; Blockchains; Servers; Databases; Artificial intelligence; Accountability; AI; Index Terms; blockchain; fairness; federated learning; machine learning; responsible AI; smart contract

【发表时间】2023 15-Feb

【收录时间】2023-05-09

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-联邦学习