文献类型：会议论文

英文题名：Blockchain Empowered Reliable Federated Learning by Worker Selection : A Trustworthy Reputation Evaluation Method

作者：Zhang, Qinnan[1];Ding, Qingyang[2];Zhu, Jianming[1];Li, Dandan[2]

第一作者：Zhang, Qinnan

通讯作者：Ding, QY[1]

机构：[1]Cent Univ Finance & Econ, Sch Informat, Beijing, Peoples R China;[2]Beijing Union Univ, Sch Management, Beijing, Peoples R China

第一机构：Cent Univ Finance & Econ, Sch Informat, Beijing, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Sch Management, Beijing, Peoples R China.|[1141755]北京联合大学管理学院;[11417]北京联合大学;

会议论文集：IEEE Wireless Communications and Networking Conference (WCNC)

会议日期：MAR 29-APR 01, 2021

会议地点：Nanjing, PEOPLES R CHINA

语种：英文

外文关键词：blockchain; federated learning; reputation evaluation; consensus algorithm

摘要：Federated learning is a distributed machine learning framework that enables distributed model training with local datasets, which can effectively protect the data privacy of workers (i.e., intelligent edge nodes). The majority of federated learning algorithms assume that the workers are trusted and voluntarily participate in the cooperative model training process. However, the situation in practical application is not consistent with this. There are many challenges such as worker selection schemes for participating workers, which hamper the widespread adoption of federated learning. The existing research about worker selection scheme focused on multi-weight subjective logic model to calculate reputation value and adopted contract theory to motivate workers, which may exist subjective judgmental factors and unfair profit distribution. To address above challenges, we calculate the reputation value by model quality parameters to evaluate the reliability of workers. Blockchain is designed to store historical reputation value that realized tamper-resistance and non-repudiation. Numerical results indicate that the worker selection scheme can improve the accuracy of the model and accelerate the model convergence.