# Personalized Federated X -armed Bandit

arXiv: https://arxiv.org/abs/2310.16323 · HTML: https://mlsanity.com/paper/2310.16323/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [On Kernelized Multi-armed Bandits](https://mlsanity.com/paper/1704.00445/) (2017)
- [Neural Thompson Sampling](https://mlsanity.com/paper/2010.00827/) (2020)
- [Differentially-Private Federated Linear Bandits](https://mlsanity.com/paper/2010.11425/) (2020)
- [Federated Bayesian Optimization via Thompson Sampling](https://mlsanity.com/paper/2010.10154/) (2020)
- [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 Multi-Armed Bandits](https://mlsanity.com/paper/2101.12204/) (2021)
- [Federated Bandit: A Gossiping Approach](https://mlsanity.com/paper/2010.12763/) (2020)
- [A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption](https://mlsanity.com/paper/1810.00997/) (2018)
- [Lipschitz Bandits with Batched Feedback](https://mlsanity.com/paper/2110.09722/) (2021)
- [Federated Neural Bandits](https://mlsanity.com/paper/2205.14309/) (2022)
- [Communication Efficient Distributed Learning for Kernelized Contextual Bandits](https://mlsanity.com/paper/2206.04835/) (2022)
- [Online Stochastic Optimization under Correlated Bandit Feedback](https://mlsanity.com/paper/1402.0562/) (2014)
- [Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization](https://mlsanity.com/paper/2106.09215/) (2021)
- [PyXAB -- A Python Library for X-Armed Bandit and Online Blackbox Optimization Algorithms](https://mlsanity.com/paper/2303.04030/) (2023)

## Abstract (arXiv)

In this work, we study the personalized federated X-armed bandit problem, where the heterogeneous local objectives of the clients are optimized simultaneously in the federated learning paradigm. We propose the textttPF-PNE algorithm with a unique double elimination strategy, which safely eliminates the non-optimal regions while encouraging federated collaboration through biased but effective evaluations of the local objectives. The proposed textttPF-PNE algorithm is able to optimize local objectives with arbitrary levels of heterogeneity, and its limited communications protects the confidentiality of the client-wise reward data. Our theoretical analysis shows the benefit of the proposed algorithm over single-client algorithms. Experimentally, textttPF-PNE outperforms multiple baselines on both synthetic and real life datasets.