  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.
