# Federated Linear Contextual Bandits

arXiv: https://arxiv.org/abs/2110.14177 · HTML: https://mlsanity.com/paper/2110.14177/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 64 references that are in the map.
- First posted on arXiv 27 Oct 2021.

## Builds on (most cited first, 23 of 23)

- [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)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Finite-Time Analysis of Kernelised Contextual Bandits](https://mlsanity.com/paper/1309.6869/) (2013)
- [Online Clustering of Bandits](https://mlsanity.com/paper/1401.8257/) (2014)
- [Batched Multi-armed Bandits Problem](https://mlsanity.com/paper/1904.01763/) (2019)
- [Differentially-Private Federated Linear Bandits](https://mlsanity.com/paper/2010.11425/) (2020)
- [Stochastic Linear Bandits Robust to Adversarial Attacks](https://mlsanity.com/paper/2007.03285/) (2020)
- [Sequential Batch Learning in Finite-Action Linear Contextual Bandits](https://mlsanity.com/paper/2004.06321/) (2020)
- [Federated Multi-armed Bandits with Personalization](https://mlsanity.com/paper/2102.13101/) (2021)
- [Federated Multi-Armed Bandits](https://mlsanity.com/paper/2101.12204/) (2021)
- [Federated Bandit: A Gossiping Approach](https://mlsanity.com/paper/2010.12763/) (2020)
- [Distributed Clustering of Linear Bandits in Peer to Peer Networks](https://mlsanity.com/paper/1604.07706/) (2016)
- [A Gang of Bandits](https://mlsanity.com/paper/1306.0811/) (2013)
- [SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits](https://mlsanity.com/paper/1809.08151/) (2018)
- [On Context-Dependent Clustering of Bandits](https://mlsanity.com/paper/1608.03544/) (2016)
- [On Distributed Cooperative Decision-Making in Multiarmed Bandits](https://mlsanity.com/paper/1512.06888/) (2015)
- [Federated Recommendation System via Differential Privacy](https://mlsanity.com/paper/2005.06670/) (2020)
- [Decentralized Multi-player Multi-armed Bandits with No Collision Information](https://mlsanity.com/paper/2003.00162/) (2020)
- [Concurrent bandits and cognitive radio networks](https://mlsanity.com/paper/1404.5421/) (2014)
- [Estimation Considerations in Contextual Bandits](https://mlsanity.com/paper/1711.07077/) (2017)
- [Federated Residual Learning](https://mlsanity.com/paper/2003.12880/) (2020)
- [The Externalities of Exploration and How Data Diversity Helps Exploitation](https://mlsanity.com/paper/1806.00543/) (2018)

## Built on by (most cited first, 30 of 61)

- [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)
- [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)
- [On Differentially Private Federated Linear Contextual Bandits](https://mlsanity.com/paper/2302.13945/) (2023)
- [Collaborative Pure Exploration in Kernel Bandit](https://mlsanity.com/paper/2110.15771/) (2021)
- [Communication Efficient Federated Learning for Generalized Linear Bandits](https://mlsanity.com/paper/2202.01087/) (2022)
- [Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost](https://mlsanity.com/paper/2205.13170/) (2022)
- [Federated Linear Contextual Bandits with User-level Differential Privacy](https://mlsanity.com/paper/2306.05275/) (2023)
- [Online Clustering of Bandits with Misspecified User Models](https://mlsanity.com/paper/2310.02717/) (2023)
- [Decentralized Randomly Distributed Multi-agent Multi-armed Bandit with Heterogeneous Rewards](https://mlsanity.com/paper/2306.05579/) (2023)
- [Linear Stochastic Bandits over a Bit-Constrained Channel](https://mlsanity.com/paper/2203.01198/) (2022)
- [Byzantine-Robust Federated Linear Bandits](https://mlsanity.com/paper/2204.01155/) (2022)
- [Differentially Private Linear Bandits with Partial Distributed Feedback](https://mlsanity.com/paper/2207.05827/) (2022)
- [Distributed Linear Bandits under Communication Constraints](https://mlsanity.com/paper/2211.02212/) (2022)
- [LLMs-augmented Contextual Bandit](https://mlsanity.com/paper/2311.02268/) (2023)
- [A Multi-Agent Conversational Bandit Approach to Online Evaluation and Selection of User-Aligned LLM Responses](https://mlsanity.com/paper/2501.01849/) (2025)
- [Doubly Adversarial Federated Bandits](https://mlsanity.com/paper/2301.09223/) (2023)
- [Federated Online and Bandit Convex Optimization](https://mlsanity.com/paper/2311.17586/) (2023)
- [Federated Multi-Armed Bandits Under Byzantine Attacks](https://mlsanity.com/paper/2205.04134/) (2022)
- [Collaborative Learning in Kernel-based Bandits for Distributed Users](https://mlsanity.com/paper/2207.07948/) (2022)
- [Pure Exploration in Asynchronous Federated Bandits](https://mlsanity.com/paper/2310.11015/) (2023)
- [On the Necessity of Collaboration for Online Model Selection with Decentralized Data](https://mlsanity.com/paper/2404.09494/) (2024)
- [FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients](https://mlsanity.com/paper/2405.02881/) (2024)
- [Federated X-Armed Bandit](https://mlsanity.com/paper/2205.15268/) (2022)
- [On the Effect of Defections in Federated Learning and How to Prevent Them](https://mlsanity.com/paper/2311.16459/) (2023)
- [Lifelong Bandit Optimization: No Prior and No Regret](https://mlsanity.com/paper/2210.15513/) (2022)
- [Federated Linear Contextual Bandits with Heterogeneous Clients](https://mlsanity.com/paper/2403.00116/) (2024)
- [Federated Online Sparse Decision Making](https://mlsanity.com/paper/2202.13448/) (2022)
- [Differentially Private Federated Combinatorial Bandits with Constraints](https://mlsanity.com/paper/2206.13192/) (2022)

## Abstract (arXiv)

This paper presents a novel federated linear contextual bandits model, where individual clients face different K-armed stochastic bandits coupled through common global parameters. By leveraging the geometric structure of the linear rewards, a collaborative algorithm called Fed-PE is proposed to cope with the heterogeneity across clients without exchanging local feature vectors or raw data. Fed-PE relies on a novel multi-client G-optimal design, and achieves near-optimal regrets for both disjoint and shared parameter cases with logarithmic communication costs. In addition, a new concept called collinearly-dependent policies is introduced, based on which a tight minimax regret lower bound for the disjoint parameter case is derived. Experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real-world datasets.