# Differentially Private Federated Combinatorial Bandits with Constraints

arXiv: https://arxiv.org/abs/2206.13192 · HTML: https://mlsanity.com/paper/2206.13192/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 51 references that are in the map.
- First posted on arXiv 27 Jun 2022.

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

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data](https://mlsanity.com/paper/1610.05755/) (2016)
- [Differentially-Private Federated Linear Bandits](https://mlsanity.com/paper/2010.11425/) (2020)
- [Thompson Sampling for Combinatorial Semi-Bandits](https://mlsanity.com/paper/1803.04623/) (2018)
- [Federated Multi-armed Bandits with Personalization](https://mlsanity.com/paper/2102.13101/) (2021)
- [Federated Linear Contextual Bandits](https://mlsanity.com/paper/2110.14177/) (2021)
- [Federated Learning with Bayesian Differential Privacy](https://mlsanity.com/paper/1911.10071/) (2019)
- [Federated Multi-Armed Bandits](https://mlsanity.com/paper/2101.12204/) (2021)
- [Artificial Intelligence and Statistics](https://mlsanity.com/paper/1712.03779/) (2017)
- [Privacy-Preserving Communication-Efficient Federated Multi-Armed Bandits](https://mlsanity.com/paper/2111.01570/) (2021)
- [Federated Learning Meets Fairness and Differential Privacy](https://mlsanity.com/paper/2108.09932/) (2021)
- [Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits](https://mlsanity.com/paper/2012.03270/) (2020)
- [Privacy-Preserving Bandits](https://mlsanity.com/paper/1909.04421/) (2019)
- [Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits](https://mlsanity.com/paper/2106.13895/) (2021)
- [A Multi-Arm Bandit Approach To Subset Selection Under Constraints](https://mlsanity.com/paper/2102.04824/) (2021)

## Built on by (most cited first, 3 of 3)

- [Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation](https://mlsanity.com/paper/2401.15874/) (2024)
- [Fixed-Budget Differentially Private Best Arm Identification](https://mlsanity.com/paper/2401.09073/) (2024)
- [Fairness and Privacy Guarantees in Federated Contextual Bandits](https://mlsanity.com/paper/2402.03531/) (2024)

## Abstract (arXiv)

There is a rapid increase in the cooperative learning paradigm in online learning settings, i.e., federated learning (FL). Unlike most FL settings, there are many situations where the agents are competitive. Each agent would like to learn from others, but the part of the information it shares for others to learn from could be sensitive; thus, it desires its privacy. This work investigates a group of agents working concurrently to solve similar combinatorial bandit problems while maintaining quality constraints. Can these agents collectively learn while keeping their sensitive information confidential by employing differential privacy? We observe that communicating can reduce the regret. However, differential privacy techniques for protecting sensitive information makes the data noisy and may deteriorate than help to improve regret. Hence, we note that it is essential to decide when to communicate and what shared data to learn to strike a functional balance between regret and privacy. For such a federated combinatorial MAB setting, we propose a Privacy-preserving Federated Combinatorial Bandit algorithm, P-FCB. We illustrate the efficacy of P-FCB through simulations. We further show that our algorithm provides an improvement in terms of regret while upholding quality threshold and meaningful privacy guarantees.