A user (model owner) in federated learning builds a learning model by aggregating local learning models trained by independent workers with their private datasets. A fundamental issue of federating learning is allocating resource from workers to the training task. As the allocation causes extra costs and overheads, workers are inherently reluctant to participate. Therefore, it is crucial to design an incentive-based resource allocation mechanism (incentive mechanism) that motivates workers to contribute their resources. Though some incentive mechanisms have been proposed for federating learning, none has devoted to the case when multiple users coexist and compete for worker service whereas a worker can contribute to multiple training tasks at the same time. For this scenario, this paper proposes an auction-based approach, where multiple users as buyers place bids for worker's service. We devise two algorithms attempting to find an auction result that maximizes social welfare, together with a pricing rule that ensures incentive compatibility and individual rationality. Simulation results show that one of the algorithms, which is based on the alternating direction method of multipliers (ADMM), outperforms the other greedy algorithm in terms of social welfare particularly when workers do not have adequate computing resource for all the training tasks.

中文翻译：

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联邦学习中多模型所有者的激励感知资源分配

联邦学习中的用户（模型所有者）通过将独立工作者训练的本地学习模型与其私有数据集聚合来构建学习模型。联邦学习的一个基本问题是将工作人员的资源分配给培训任务。由于分配会带来额外的成本和管理费用，工人本来就不愿意参与。因此，设计一种基于激励的资源配置机制（激励机制），激励员工贡献自己的资源至关重要。尽管已经提出了一些联邦学习的激励机制，但没有一个专门针对多个用户共存并竞争工作者服务而一个工作者可以同时为多个训练任务做出贡献的情况。对于这种情况，本文提出了一种基于拍卖的方法，其中多个用户作为买家对工人的服务进行出价。我们设计了两种算法，试图找到使社会福利最大化的拍卖结果，以及确保激励兼容性和个人理性的定价规则。仿真结果表明，其中一种基于乘子交替方向法（ADMM）的算法在社会福利方面优于另一种贪心算法，特别是当工人没有足够的计算资源来完成所有训练任务时。