Federated learning (FL) enables multiple data owners (a.k.a. FL clients) to collaboratively train machine learning models without disclosing sensitive private data. Existing FL research mostly focuses on the monopoly scenario in which a single FL server selects a subset of FL clients to update their local models in each round of training. In practice, there can be multiple FL servers simultaneously trying to select clients from the same pool. In this paper, we propose a first-of-its-kind Fairness-aware Federated Job Scheduling (FairFedJS) approach to bridge this gap. Based on Lyapunov optimization, it ensures fair allocation of high-demand FL client datasets to FL jobs in need of them, by jointly considering the current demand and the job payment bids, in order to prevent prolonged waiting. Extensive experiments comparing FairFedJS against four state-of-the-art approaches on two datasets demonstrate its significant advantages. It outperforms the best baseline by 31.9% and 1.0% on average in terms of scheduling fairness and convergence time, respectively, while achieving comparable test accuracy.

翻译：联邦学习（FL）使多个数据持有者（即FL客户端）能够在无需披露敏感私有数据的情况下协作训练机器学习模型。现有FL研究主要集中于单一场景，即单个FL服务器在每轮训练中从客户端池中选取子集以更新其本地模型。然而实际中可能存在多个FL服务器同时试图从同一客户端池中选择客户端的情况。本文首次提出了一种公平感知的联邦任务调度方法（FairFedJS），以弥合这一差距。该方法基于Lyapunov优化，通过联合考虑当前需求与任务支付投标，确保高需求FL客户端数据集被公平分配给需要这些数据的FL任务，从而避免长期等待。在两个数据集上对比FairFedJS与四种最先进方法的广泛实验表明，该方法具有显著优势。在调度公平性和收敛时间方面，相较最优基线平均分别提升31.9%和1.0%，同时达到相当的测试精度。