Federated Learning (FL) is a distributed learning framework that can deal with the distributed issue in machine learning and still guarantee high learning performance. However, it is impractical that all users will sacrifice their resources to join the FL algorithm. This motivates us to study the incentive mechanism design for FL. In this paper, we consider a FL system that involves one base station (BS) and multiple mobile users. The mobile users use their own data to train the local machine learning model, and then send the trained models to the BS, which generates the initial model, collects local models and constructs the global model. Then, we formulate the incentive mechanism between the BS and mobile users as an auction game where the BS is an auctioneer and the mobile users are the sellers. In the proposed game, each mobile user submits its bids according to the minimal energy cost that the mobile users experiences in participating in FL. To decide winners in the auction and maximize social welfare, we propose the primal-dual greedy auction mechanism. The proposed mechanism can guarantee three economic properties, namely, truthfulness, individual rationality and efficiency. Finally, numerical results are shown to demonstrate the performance effectiveness of our proposed mechanism.

中文翻译：

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无线蜂窝网络中联合学习的激励机制：一种拍卖方法

联邦学习（FL）是一种分布式学习框架，可以在处理机器学习中的分布式问题的同时，仍然保证较高的学习性能。但是，让所有用户牺牲自己的资源加入 FL 算法是不切实际的。这促使我们研究FL的激励机制设计。在本文中，我们考虑涉及一个基站 (BS) 和多个移动用户的 FL 系统。移动用户使用自己的数据训练本地机器学习模型，然后将训练好的模型发送到 BS，由 BS 生成初始模型，收集本地模型并构建全局模型。然后，我们将 BS 和移动用户之间的激励机制制定为一个拍卖游戏，其中 BS 是拍卖者，移动用户是卖家。在提议的比赛中，每个移动用户根据参与 FL 的移动用户体验的最低能源成本提交其投标。为了在拍卖中决定获胜者并最大化社会福利，我们提出了原始对偶贪婪拍卖机制。所提出的机制可以保证三个经济属性，即真实性、个体理性和效率。最后，数值结果证明了我们提出的机制的性能有效性。