# Federated Online Sparse Decision Making

arXiv: https://arxiv.org/abs/2202.13448 · HTML: https://mlsanity.com/paper/2202.13448/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 27 Feb 2022.

## Builds on (most cited first, 21 of 21)

- [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)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Exploiting Shared Representations for Personalized Federated Learning](https://mlsanity.com/paper/2102.07078/) (2021)
- [Differentially-Private Federated Linear Bandits](https://mlsanity.com/paper/2010.11425/) (2020)
- [What Doubling Tricks Can and Can't Do for Multi-Armed Bandits](https://mlsanity.com/paper/1803.06971/) (2018)
- [Federated Multi-armed Bandits with Personalization](https://mlsanity.com/paper/2102.13101/) (2021)
- [High-Dimensional Sparse Linear Bandits](https://mlsanity.com/paper/2011.04020/) (2020)
- [Federated Linear Contextual Bandits](https://mlsanity.com/paper/2110.14177/) (2021)
- [Federated Bandit: A Gossiping Approach](https://mlsanity.com/paper/2010.12763/) (2020)
- [Doubly-Robust Lasso Bandit](https://mlsanity.com/paper/1907.11362/) (2019)
- [Sparsity-Agnostic Lasso Bandit](https://mlsanity.com/paper/2007.08477/) (2020)
- [Federated Recommendation System via Differential Privacy](https://mlsanity.com/paper/2005.06670/) (2020)
- [Estimation Considerations in Contextual Bandits](https://mlsanity.com/paper/1711.07077/) (2017)
- [Federated Residual Learning](https://mlsanity.com/paper/2003.12880/) (2020)
- [Online Forgetting Process for Linear Regression Models](https://mlsanity.com/paper/2012.01668/) (2020)
- [A Simple Unified Framework for High Dimensional Bandit Problems](https://mlsanity.com/paper/2102.09626/) (2021)
- [Residual Bootstrap Exploration for Bandit Algorithms](https://mlsanity.com/paper/2002.08436/) (2020)
- [Residual Bootstrap Exploration for Stochastic Linear Bandit](https://mlsanity.com/paper/2202.11474/) (2022)
- [Online Batch Decision-Making with High-Dimensional Covariates](https://mlsanity.com/paper/2002.09438/) (2020)
- [Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization](https://mlsanity.com/paper/2106.09215/) (2021)

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

- [Federated Linear Contextual Bandits with User-level Differential Privacy](https://mlsanity.com/paper/2306.05275/) (2023)
- [Federated X-Armed Bandit](https://mlsanity.com/paper/2205.15268/) (2022)
- [Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups](https://mlsanity.com/paper/2409.19092/) (2024)

## Abstract (arXiv)

This paper presents a novel federated linear contextual bandits model, where individual clients face different K-armed stochastic bandits with high-dimensional decision context and coupled through common global parameters. By leveraging the sparsity structure of the linear reward , a collaborative algorithm named textttFedego Lasso is proposed to cope with the heterogeneity across clients without exchanging local decision context vectors or raw reward data. textttFedego Lasso relies on a novel multi-client teamwork-selfish bandit policy design, and achieves near-optimal regrets for shared parameter cases with logarithmic communication costs. In addition, a new conceptual tool called federated-egocentric policies is introduced to delineate exploration-exploitation trade-off. Experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real-world datasets.