论文标题
估计单个设备贡献以激励联合学习
Estimation of Individual Device Contributions for Incentivizing Federated Learning
论文作者
论文摘要
联合学习（FL）是一种新兴技术，用于使用移动设备的数据和计算资源进行协作训练机器学习模型，而无需暴露对隐私敏感的用户数据。 促使数据和移动设备所有者参与FL的适当激励机制是为FL构建可持续平台的关键。但是，很难评估设备/所有者的贡献水平，以确定适当的奖励，而无需大量的计算和通信开销。 本文提出了一种估计参与设备贡献水平的计算和沟通效率方法。提出的方法通过减少对流量和计算开销的需求，可以在单个FL训练过程中进行此类估计。使用MNIST数据集进行的绩效评估表明，所提出的方法可以准确地估算单个参与者的贡献，而计算少于46-49％，而没有与幼稚估计方法相比，没有通信开销。
Federated learning (FL) is an emerging technique used to train a machine-learning model collaboratively using the data and computation resource of the mobile devices without exposing privacy-sensitive user data. Appropriate incentive mechanisms that motivate the data and mobile-device owner to participate in FL is key to building a sustainable platform for FL. However, it is difficult to evaluate the contribution level of the devices/owners to determine appropriate rewards without large computation and communication overhead. This paper proposes a computation-and communication-efficient method of estimating a participating device's contribution level. The proposed method enables such estimation during a single FL training process, there by reducing the need for traffic and computation overhead. The performance evaluations using the MNIST dataset show that the proposed method estimates individual participants' contributions accurately with 46-49% less computation overhead and no communication overhead than a naive estimation method.
