# Fairness and Privacy Guarantees in Federated Contextual Bandits

arXiv: https://arxiv.org/abs/2402.03531 · HTML: https://mlsanity.com/paper/2402.03531/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 47 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 19 of 19)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [Differentially-Private Federated Linear Bandits](https://mlsanity.com/paper/2010.11425/) (2020)
- [Differentially Private Contextual Linear Bandits](https://mlsanity.com/paper/1810.00068/) (2018)
- [Achieving Fairness in the Stochastic Multi-armed Bandit Problem](https://mlsanity.com/paper/1907.10516/) (2019)
- [Algorithms for Differentially Private Multi-Armed Bandits](https://mlsanity.com/paper/1511.08681/) (2015)
- [Fairness of Exposure in Stochastic Bandits](https://mlsanity.com/paper/2103.02735/) (2021)
- [Fair Contextual Multi-Armed Bandits: Theory and Experiments](https://mlsanity.com/paper/1912.08055/) (2019)
- [Fair Algorithms for Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2007.06699/) (2020)
- [A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits](https://mlsanity.com/paper/2207.03106/) (2022)
- [Near-Optimal Collaborative Learning in Bandits](https://mlsanity.com/paper/2206.00121/) (2022)
- [Fair Algorithms for Infinite and Contextual Bandits](https://mlsanity.com/paper/1610.09559/) (2016)
- [Federated Online Clustering of Bandits](https://mlsanity.com/paper/2208.14865/) (2022)
- [Designing Truthful Contextual Multi-Armed Bandits based Sponsored Search Auctions](https://mlsanity.com/paper/2002.11349/) (2020)
- [A Multi-Arm Bandit Approach To Subset Selection Under Constraints](https://mlsanity.com/paper/2102.04824/) (2021)
- [Differentially Private Federated Combinatorial Bandits with Constraints](https://mlsanity.com/paper/2206.13192/) (2022)
- [Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks](https://mlsanity.com/paper/2303.00799/) (2023)
- [A Regret bound for Non-stationary Multi-Armed Bandits with Fairness Constraints](https://mlsanity.com/paper/2012.13380/) (2020)
- [Asymptotic Existence of Proportionally Fair Allocations](https://mlsanity.com/paper/1806.00218/) (2018)

## Built on by (most cited first, 1 of 1)

- [Federated Linear Dueling Bandits](https://mlsanity.com/paper/2502.01085/) (2025)

## Abstract (arXiv)

This paper considers the contextual multi-armed bandit (CMAB) problem with fairness and privacy guarantees in a federated environment. We consider merit-based exposure as the desired fair outcome, which provides exposure to each action in proportion to the reward associated. We model the algorithm's effectiveness using fairness regret, which captures the difference between fair optimal policy and the policy output by the algorithm. Applying fair CMAB algorithm to each agent individually leads to fairness regret linear in the number of agents. We propose that collaborative -- federated learning can be more effective and provide the algorithm Fed-FairX-LinUCB that also ensures differential privacy. The primary challenge in extending the existing privacy framework is designing the communication protocol for communicating required information across agents. A naive protocol can either lead to weaker privacy guarantees or higher regret. We design a novel communication protocol that allows for (i) Sub-linear theoretical bounds on fairness regret for Fed-FairX-LinUCB and comparable bounds for the private counterpart, Priv-FairX-LinUCB (relative to single-agent learning), (ii) Effective use of privacy budget in Priv-FairX-LinUCB. We demonstrate the efficacy of our proposed algorithm with extensive simulations-based experiments. We show that both Fed-FairX-LinUCB and Priv-FairX-LinUCB achieve near-optimal fairness regret.