We develop an iterative differentially private algorithm for client selection in federated settings. We consider a federated network wherein clients coordinate with a central server to complete a task; however, the clients decide whether to participate or not at a time step based on their preferences -- local computation and probabilistic intent. The algorithm does not require client-to-client information exchange. The developed algorithm provides near-optimal values to the clients over long-term average participation with a certain differential privacy guarantee. Finally, we present the experimental results to check the algorithm's efficacy.

翻译：我们提出了一种用于联邦场景中客户端选择的迭代差分隐私算法。考虑一个联邦网络，其中客户端与中央服务器协作完成任务；然而，客户端会根据其偏好（本地计算能力和概率意图）决定是否在某个时间步参与。该算法无需客户端间信息交换。所提出的算法在长期平均参与度上为客户端提供近乎最优的值，并具有特定的差分隐私保证。最后，我们通过实验结果验证了该算法的有效性。