问责
计算机科学
可信赖性
联合学习
数据建模
计算机安全
块链
建筑
一般化
分布式计算
机器学习
人工智能
数据库
艺术
政治学
法学
视觉艺术
数学分析
数学
作者
Sin Kit Lo,Yue Liu,Qinghua Lu,Chen Wang,Xiwei Xu,Hye-Young Paik,Liming Zhu
标识
DOI:10.1109/jiot.2022.3144450
摘要
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.
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