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.

通过 Lyapunov 优化的公平感知联邦用户选择（FairFedCS）方法可以在联邦学习中动态调整联邦用户的选择概率，根据联邦用户的声誉、参与联邦任务的次数和对最终模型性能的贡献，共同考虑性能和公平性。在真实的多媒体数据集上进行的大量实验表明，FairFedCS 平均实现了 19.6% 更高的公平性和 0.73% 更高的测试准确性，超过了最佳性能的现有方法。