一种面向电能量数据的联邦学习可靠性激励机制

Reliable Incentive Mechanism for Federated Learning of Electric Metering Data

联邦学习解决了数据安全日益受到重视条件下的数据互用难题,但是传统联邦学习缺少鼓励和吸引数据拥有方参与到联邦学习中的激励机制,联邦学习审核机制的缺失给恶意节点进行破坏攻击提供了可能性。针对这个问题,文中提出基于区块链技术的面向电能量数据的可靠的联邦学习激励机制。该方法从对数据参与方的训练参与进行奖励和对数据参与方的数据可靠性进行评估两方面入手,设计算法对数据参与方的训练效果进行评估,从训练效果和训练成本等角度来确定数据参与方的贡献度,并根据贡献度来对参与方进行奖励,同时针对数据参与方的可靠性建立声望模型,根据训练效果对数据参与方的声望进行更新,藉此实现对数据参与方的可靠性评估。基于联邦学习开源框架和真实电能量数据进行算例分析,所得结果验证了所提方法的有效性。

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 participants 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.