In traditional machine learning, the central server first collects the data owners' private data together and then trains the model. However, people's concerns about data privacy protection are dramatically increasing. The emerging paradigm of federated learning efficiently builds machine learning models while allowing the private data to be kept at local devices. The success of federated learning requires sufficient data owners to jointly utilize their data, computing and communication resources for model training. In this paper, we propose an auction based market model for incentivizing data owners to participate in federated learning. We design two auction mechanisms for the federated learning platform to maximize the social welfare of the federated learning services market. Specifically, we first design an approximate strategy-proof mechanism which guarantees the truthfulness, individual rationality, and computational efficiency. To improve the social welfare, we develop an automated strategy-proof mechanism based on deep reinforcement learning and graph neural networks. The communication traffic congestion and the unique characteristics of federated learning are particularly considered in the proposed model. Extensive experimental results demonstrate that our proposed auction mechanisms can efficiently maximize the social welfare and provide effective insights and strategies for the platform to organize the federated training.

中文翻译：

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面向无线联合学习服务市场的自动化拍卖框架

在传统机器学习中，中央服务器首先将数据所有者的私有数据收集在一起，然后训练模型。然而，人们对数据隐私保护的担忧正在急剧增加。新兴的联邦学习范式有效地构建了机器学习模型，同时允许将私有数据保存在本地设备中。联邦学习的成功需要足够的数据所有者共同利用他们的数据、计算和通信资源进行模型训练。在本文中，我们提出了一种基于拍卖的市场模型，用于激励数据所有者参与联邦学习。我们为联邦学习平台设计了两种拍卖机制，以最大化联邦学习服务市场的社会福利。具体来说，我们首先设计了一个近似的策略证明机制，保证真实性、个体合理性和计算效率。为了提高社会福利，我们开发了一种基于深度强化学习和图神经网络的自动化策略证明机制。所提出的模型特别考虑了通信流量拥塞和联邦学习的独特特征。大量的实验结果表明，我们提出的拍卖机制可以有效地最大化社会福利，并为平台组织联合训练提供有效的见解和策略。我们开发了一种基于深度强化学习和图神经网络的自动化策略证明机制。所提出的模型特别考虑了通信流量拥塞和联邦学习的独特特征。大量的实验结果表明，我们提出的拍卖机制可以有效地最大化社会福利，并为平台组织联合训练提供有效的见解和策略。我们开发了一种基于深度强化学习和图神经网络的自动化策略证明机制。所提出的模型特别考虑了通信流量拥塞和联邦学习的独特特征。大量的实验结果表明，我们提出的拍卖机制可以有效地最大化社会福利，并为平台组织联合训练提供有效的见解和策略。