# Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits

arXiv: https://arxiv.org/abs/2110.01463 · HTML: https://mlsanity.com/paper/2110.01463/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 4 Oct 2021.

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

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Online Clustering of Bandits](https://mlsanity.com/paper/1401.8257/) (2014)
- [Differentially-Private Federated Linear Bandits](https://mlsanity.com/paper/2010.11425/) (2020)
- [Communication Complexity of Distributed Convex Learning and Optimization](https://mlsanity.com/paper/1506.01900/) (2015)
- [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)
- [Improved Algorithm on Online Clustering of Bandits](https://mlsanity.com/paper/1902.09162/) (2019)
- [Perturbed-History Exploration in Stochastic Linear Bandits](https://mlsanity.com/paper/1903.09132/) (2019)
- [Fast Distributed Bandits for Online Recommendation Systems](https://mlsanity.com/paper/2007.08061/) (2020)
- [Laplacian-regularized graph bandits: Algorithms and theoretical analysis](https://mlsanity.com/paper/1907.05632/) (2019)
- [Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication](https://mlsanity.com/paper/1401.4432/) (2014)
- [Unifying Clustered and Non-stationary Bandits](https://mlsanity.com/paper/2009.02463/) (2020)
- [Distributed Optimization for Second-Order Multi-Agent Systems with Dynamic Event-Triggered Communication](https://mlsanity.com/paper/1803.06380/) (2018)

## Built on by (most cited first, 30 of 31)

- [A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits](https://mlsanity.com/paper/2207.03106/) (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)
- [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)
- [Federated Linear Contextual Bandits with User-level Differential Privacy](https://mlsanity.com/paper/2306.05275/) (2023)
- [Federated Combinatorial Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2405.05950/) (2024)
- [Federated Online Clustering of Bandits](https://mlsanity.com/paper/2208.14865/) (2022)
- [Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation](https://mlsanity.com/paper/2305.06446/) (2023)
- [On-Demand Communication for Asynchronous Multi-Agent Bandits](https://mlsanity.com/paper/2302.07446/) (2023)
- [Federated Online and Bandit Convex Optimization](https://mlsanity.com/paper/2311.17586/) (2023)
- [Pure Exploration in Asynchronous Federated Bandits](https://mlsanity.com/paper/2310.11015/) (2023)
- [Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users](https://mlsanity.com/paper/2402.16312/) (2024)
- [FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients](https://mlsanity.com/paper/2405.02881/) (2024)
- [Federated Linear Contextual Bandits with Heterogeneous Clients](https://mlsanity.com/paper/2403.00116/) (2024)
- [Incentivized Communication for Federated Bandits](https://mlsanity.com/paper/2309.11702/) (2023)
- [Federated Linear Bandits with Finite Adversarial Actions](https://mlsanity.com/paper/2311.00973/) (2023)
- [Incentivized Truthful Communication for Federated Bandits](https://mlsanity.com/paper/2402.04485/) (2024)
- [Federated Linear Dueling Bandits](https://mlsanity.com/paper/2502.01085/) (2025)
- [Communication-Efficient Federated Non-Linear Bandit Optimization](https://mlsanity.com/paper/2311.01695/) (2023)
- [Harnessing the Power of Federated Learning in Federated Contextual Bandits](https://mlsanity.com/paper/2312.16341/) (2023)
- [Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups](https://mlsanity.com/paper/2409.19092/) (2024)
- [Batched Stochastic Linear Bandits with 1-Bit Communication Constraints](https://mlsanity.com/paper/2605.30976/) (2026)
- [Vertical Federated Linear Contextual Bandits](https://mlsanity.com/paper/2210.11050/) (2022)
- [Reward Teaching for Federated Multi-armed Bandits](https://mlsanity.com/paper/2305.02441/) (2023)
- [FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning](https://mlsanity.com/paper/2405.20504/) (2024)
- [Multi-Agent Best Arm Identification in Stochastic Linear Bandits](https://mlsanity.com/paper/2411.13690/) (2024)
- [On the optimal regret of collaborative personalized linear bandits](https://mlsanity.com/paper/2506.15943/) (2025)
- [Decentralized Contextual Bandits with Network Adaptivity](https://mlsanity.com/paper/2508.13411/) (2025)
- [Anomaly Detection in Networked Bandits](https://mlsanity.com/paper/2508.20076/) (2025)

## Abstract (arXiv)

Linear contextual bandit is a popular online learning problem. It has been mostly studied in centralized learning settings. With the surging demand of large-scale decentralized model learning, e.g., federated learning, how to retain regret minimization while reducing communication cost becomes an open challenge. In this paper, we study linear contextual bandit in a federated learning setting. We propose a general framework with asynchronous model update and communication for a collection of homogeneous clients and heterogeneous clients, respectively. Rigorous theoretical analysis is provided about the regret and communication cost under this distributed learning framework; and extensive empirical evaluations demonstrate the effectiveness of our solution.