关键词: 电力计量, 激励机制, 可靠性, 联邦学习, 区块链, 声望模型

Abstract: Federated learning has solved the problem of data interoperability under the premise of satisfying user privacy protection and data security.However,traditional federated learning lacks an incentive mechanism to encourage and attract data owners to participate in federated learning.Meanwhile,the lack of a federated learning audit mechanism provides the possibility for malicious nodes to conduct sabotage attacks.In response to this problem,this paper proposes a reliable federated learning incentive mechanism for electric metering data based on blockchain technology.This method starts from two aspects:rewarding data parti-cipants for training participation and evaluating data reliability for all of them.We design an algorithm to evaluate the training effect of data participants.The contribution of data participants is determined from the perspective of training effect and training cost,and the participants are rewarded according to the contribution.At the same time,a reputation model is established for the reliability of the data participants,and the reputation of the data participants is updated according to the training effect,so as to achieve the reliability assessment for data participants.Based on the open-source framework of federated learning and real electric metering data,a case study is carried out,and the obtained results verify the effectiveness of our method.

Key words: Blockchain, Electricity metering, Federated learning, Incentive mechanism, Reliability, Reputation model