# Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users

arXiv: https://arxiv.org/abs/2402.16312 · HTML: https://mlsanity.com/paper/2402.16312/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 15 of 15)

- [Online Clustering of Bandits](https://mlsanity.com/paper/1401.8257/) (2014)
- [Differentially-Private Federated Linear Bandits](https://mlsanity.com/paper/2010.11425/) (2020)
- [Cascading Bandits: Learning to Rank in the Cascade Model](https://mlsanity.com/paper/1502.02763/) (2015)
- [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)
- [Cascading Bandits for Large-Scale Recommendation Problems](https://mlsanity.com/paper/1603.05359/) (2016)
- [Combinatorial Cascading Bandits](https://mlsanity.com/paper/1507.04208/) (2015)
- [Improved Algorithm on Online Clustering of Bandits](https://mlsanity.com/paper/1902.09162/) (2019)
- [Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits](https://mlsanity.com/paper/2110.01463/) (2021)
- [A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits](https://mlsanity.com/paper/2207.03106/) (2022)
- [Online Clustering of Contextual Cascading Bandits](https://mlsanity.com/paper/1711.08594/) (2017)
- [Contextual Combinatorial Bandits with Probabilistically Triggered Arms](https://mlsanity.com/paper/2303.17110/) (2023)
- [Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms](https://mlsanity.com/paper/2208.14837/) (2022)
- [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)

## Built on by (most cited first, 6 of 6)

- [FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients](https://mlsanity.com/paper/2405.02881/) (2024)
- [Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contexts](https://mlsanity.com/paper/2501.00891/) (2025)
- [Online Clustering of Dueling Bandits](https://mlsanity.com/paper/2502.02079/) (2025)
- [Federated Linear Dueling Bandits](https://mlsanity.com/paper/2502.01085/) (2025)
- [Heterogeneous Multi-agent Multi-armed Bandits on Stochastic Block Models](https://mlsanity.com/paper/2502.08003/) (2025)
- [Scaling Federated Linear Contextual Bandits via Sketching](https://mlsanity.com/paper/2605.00500/) (2026)

## Abstract (arXiv)

We study the problem of federated contextual combinatorial cascading bandits, where |U| agents collaborate under the coordination of a central server to provide tailored recommendations to the |U| corresponding users. Existing works consider either a synchronous framework, necessitating full agent participation and global synchronization, or assume user homogeneity with identical behaviors. We overcome these limitations by considering (1) federated agents operating in an asynchronous communication paradigm, where no mandatory synchronization is required and all agents communicate independently with the server, (2) heterogeneous user behaviors, where users can be stratified into J ≤ |U| latent user clusters, each exhibiting distinct preferences. For this setting, we propose a UCB-type algorithm with delicate communication protocols. Through theoretical analysis, we give sub-linear regret bounds on par with those achieved in the synchronous framework, while incurring only logarithmic communication costs. Empirical evaluation on synthetic and real-world datasets validates our algorithm's superior performance in terms of regrets and communication costs.