mlsanityData to 7 Oct 2026

Near-optimal Differentially Private Client Selection in Federated Settings

arXiv 2310.09370 ↗First posted 13 Oct 2023cs.CR cs.AI cs.DCcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 13 Oct 2023.

Builds on

17 of 36 references.

References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2014RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Responsecited by 262
  4. 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 193
  5. 2017Collecting Telemetry Data Privatelycited by 147
  6. 2022A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learningcited by 28
  7. 2014Differentially Private Distributed Optimizationcited by 23
  8. 2014Differentially Private Distributed Constrained Optimizationcited by 10
  9. 2022A Snapshot of the Frontiers of Client Selection in Federated Learningcited by 9
  10. 2020On Distributed Differential Privacy and Counting Distinct Elementscited by 8
  11. 2018Communication-efficient Distributed Multi-resource Allocationcited by 3
  12. 2015On the Design of Campus Parking Systems with QoS guaranteescited by 2
5 more
  1. 2019A Privacy-preserving Method to Optimize Distributed Resource Allocationcited by 2
  2. 2017ADMM Based Privacy-preserving Decentralized Optimizationcited by 1
  3. 2018On the Control of Agents Coupled through Shared Unit-demand Resourcescited by 1
  4. 2022Optimal Regulation of Prosumers and Consumers in Smart Energy Communitiescited by 1
  5. 2023A Communication-efficient Local Differentially Private Algorithm in Federated Optimizationcited by 1

Built on by

Not cited yet.