# Near-optimal Differentially Private Client Selection in Federated Settings

arXiv: https://arxiv.org/abs/2310.09370 · HTML: https://mlsanity.com/paper/2310.09370/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 13 Oct 2023.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Federated Optimization:Distributed Optimization Beyond the Datacenter](https://mlsanity.com/paper/1511.03575/) (2015)
- [Collecting Telemetry Data Privately](https://mlsanity.com/paper/1712.01524/) (2017)
- [A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning](https://mlsanity.com/paper/2201.02932/) (2022)
- [Differentially Private Distributed Optimization](https://mlsanity.com/paper/1401.2596/) (2014)
- [Differentially Private Distributed Constrained Optimization](https://mlsanity.com/paper/1411.4105/) (2014)
- [A Snapshot of the Frontiers of Client Selection in Federated Learning](https://mlsanity.com/paper/2210.04607/) (2022)
- [On Distributed Differential Privacy and Counting Distinct Elements](https://mlsanity.com/paper/2009.09604/) (2020)
- [Communication-efficient Distributed Multi-resource Allocation](https://mlsanity.com/paper/1807.10860/) (2018)
- [On the Design of Campus Parking Systems with QoS guarantees](https://mlsanity.com/paper/1506.02818/) (2015)
- [A Privacy-preserving Method to Optimize Distributed Resource Allocation](https://mlsanity.com/paper/1908.03080/) (2019)
- [ADMM Based Privacy-preserving Decentralized Optimization](https://mlsanity.com/paper/1707.04338/) (2017)
- [On the Control of Agents Coupled through Shared Unit-demand Resources](https://mlsanity.com/paper/1803.10386/) (2018)
- [Optimal Regulation of Prosumers and Consumers in Smart Energy Communities](https://mlsanity.com/paper/2206.12679/) (2022)
- [A Communication-efficient Local Differentially Private Algorithm in Federated Optimization](https://mlsanity.com/paper/2304.01510/) (2023)

## 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.