Federated Learning empowers data owners to collectively train an artificial intelligence model without exposing data. However, the heterogeneous resources and the self-interested users bring new challenges hindering the development of federated learning. To this end, we propose a Multi-attribute Auction-based Grouped Federated Learning scheme, called MAGFL, comprising a grouped federated learning framework and a multi-attribute auction-based group selection strategy. Initially, our grouped federated learning framework clusters clients into groups according to local characteristics. Then, we propose a quality assessment method to assess the quality of each group based on a fuzzy approach. Furthermore, the FL server distributes economic rewards to training clients to motivate more clients to join the FL system, which is likened to a multi-attribute auction market where each group agent bids for training opportunities. Moreover, we design a novel global model update method with added Adam (i.e., Adaptive Moment Estimation) operations into the global update stage, which can fully utilize the local and global update direction to accelerate the convergence rate of scheme MGAFL. Extensive experiments on real-world datasets demonstrate that the proposed scheme outperforms representative federated learning schemes (i.e., FedAvg, FedProx, and FedAvg-Adam) regarding the model's convergence rate and capacity to deal with heterogeneous systems.

中文翻译：

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基于多属性拍卖的分组联邦学习


联邦学习使数据所有者能够在不暴露数据的情况下集体训练人工智能模型。然而，资源的异构性和用户的利己主义给联邦学习的发展带来了新的挑战。为此，我们提出了一种基于多属性拍卖的分组联邦学习方案，称为MAGFL，包括分组联邦学习框架和基于多属性拍卖的组选择策略。最初，我们的分组联邦学习框架根据本地特征将客户分组。然后，我们提出了一种基于模糊方法的质量评估方法来评估每个组的质量。此外，FL服务器还向培训客户分配经济奖励，以激励更多客户加入FL系统，这就像一个多属性拍卖市场，每个群体代理竞标培训机会。此外，我们设计了一种新颖的全局模型更新方法，在全局更新阶段添加了 Adam（即自适应矩估计）操作，可以充分利用局部和全局更新方向来加速 MGAFL 方案的收敛速度。对现实世界数据集的大量实验表明，就模型的收敛速度和处理异构系统的能力而言，所提出的方案优于代表性的联邦学习方案（即 FedAvg、FedProx 和 FedAvg-Adam）。