Near-optimal Differentially Private Client Selection in Federated Settings

• URL: http://arxiv.org/abs/2310.09370v1
• Date: Fri, 13 Oct 2023 19:32:50 GMT
• Title: Near-optimal Differentially Private Client Selection in Federated Settings
• Authors: Syed Eqbal Alam, Dhirendra Shukla, and Shrisha Rao
• Abstract summary: We consider a federated network wherein clients coordinate with a central server to complete a task. The clients decide whether to participate or not at a time step based on their preferences. The developed algorithm provides near-optimal values to the clients over long-term average participation.
• Score: 3.58439716487063
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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