Federated learning (FL) has enabled multiple data owners (a.k.a. FL clients) to train machine learning models collaboratively without revealing private data. Since the FL server can only engage a limited number of clients in each training round, FL client selection has become an important research problem. Existing approaches generally focus on either enhancing FL model performance or enhancing the fair treatment of FL clients. The problem of balancing performance and fairness considerations when selecting FL clients remains open. To address this problem, we propose the Fairness-aware Federated Client Selection (FairFedCS) approach. Based on Lyapunov optimization, it dynamically adjusts FL clients' selection probabilities by jointly considering their reputations, times of participation in FL tasks and contributions to the resulting model performance. By not using threshold-based reputation filtering, it provides FL clients with opportunities to redeem their reputations after a perceived poor performance, thereby further enhancing fair client treatment. Extensive experiments based on real-world multimedia datasets show that FairFedCS achieves 19.6% higher fairness and 0.73% higher test accuracy on average than the best-performing state-of-the-art approach.

翻译：联邦学习（FL）使多个数据所有者（即FL客户端）能够在无需泄露私有数据的情况下协作训练机器学习模型。由于FL服务器每轮训练只能接入有限数量的客户端，因此FL客户端选择已成为一个重要研究问题。现有方法通常侧重于提升FL模型性能或增强对FL客户端的公平对待。在FL客户端选择中平衡性能与公平考量的问题仍未得到解决。为解决这一问题，我们提出了面向公平性的联邦客户端选择（FairFedCS）方法。该方法基于Lyapunov优化，通过联合考虑客户端的声誉、参与FL任务的次数以及其对模型性能的贡献，动态调整FL客户端的选中概率。由于未采用基于阈值的声誉过滤机制，该方法在客户端出现表现不佳后为其提供了恢复声誉的机会，从而进一步增强了客户端的公平对待。基于真实多媒体数据集的大量实验表明，FairFedCS的平均公平性比最优的现有方法高出19.6%，测试准确率平均高出0.73%。