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（Federated Learning, FL）使得多个数据所有者（即FL客户端）能够在不泄露敏感私有数据的情况下协同训练机器学习模型。现有的FL研究大多聚焦于单一垄断场景，即单个FL服务器在每轮训练中选择部分FL客户端更新其本地模型。然而在实际中，可能存在多个FL服务器同时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名为公平感知联邦作业调度（FairFedJS）的方法来填补这一空白。该方法基于李雅普诺夫优化，通过联合考虑当前需求与作业支付报价，确保高需求FL客户端数据集能够公平地分配给需要它们的FL作业，从而避免长时间等待。在两个数据集上，将FairFedJS与四种最新方法进行对比的大量实验表明，该方法具有显著优势。在调度公平性和收敛时间方面，它平均比最优基线分别高出31.9%和1.0%，同时实现了相当的测试精度。