# Differentially-Private Federated Linear Bandits

arXiv: https://arxiv.org/abs/2010.11425 · HTML: https://mlsanity.com/paper/2010.11425/

## Key facts

- Cited by 83 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 30 of 83)

- [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)
- [Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee](https://mlsanity.com/paper/2110.14074/) (2021)
- [Differentially Private Federated Bayesian Optimization with Distributed Exploration](https://mlsanity.com/paper/2110.14153/) (2021)
- [Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits](https://mlsanity.com/paper/2110.01463/) (2021)
- [Multi-Agent Multi-Armed Bandits with Limited Communication](https://mlsanity.com/paper/2102.08462/) (2021)
- [A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits](https://mlsanity.com/paper/2207.03106/) (2022)
- [Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits](https://mlsanity.com/paper/2106.02575/) (2021)
- [Differentially Private Multi-Armed Bandits in the Shuffle Model](https://mlsanity.com/paper/2106.02900/) (2021)
- [A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy](https://mlsanity.com/paper/2302.10637/) (2023)
- [Exploiting Heterogeneity in Robust Federated Best-Arm Identification](https://mlsanity.com/paper/2109.05700/) (2021)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [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)
- [Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance](https://mlsanity.com/paper/2402.01096/) (2024)
- [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)
- [Communication Efficient Parallel Reinforcement Learning](https://mlsanity.com/paper/2102.10740/) (2021)
- [A Payload Optimization Method for Federated Recommender Systems](https://mlsanity.com/paper/2107.13078/) (2021)
- [Federated Online Clustering of Bandits](https://mlsanity.com/paper/2208.14865/) (2022)
- [Linear Stochastic Bandits over a Bit-Constrained Channel](https://mlsanity.com/paper/2203.01198/) (2022)
- [Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation](https://mlsanity.com/paper/2305.06446/) (2023)
- [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)
- [Federated Best Arm Identification with Heterogeneous Clients](https://mlsanity.com/paper/2210.07780/) (2022)

## Abstract (arXiv)

The rapid proliferation of decentralized learning systems mandates the need for differentially-private cooperative learning. In this paper, we study this in context of the contextual linear bandit: we consider a collection of agents cooperating to solve a common contextual bandit, while ensuring that their communication remains private. For this problem, we devise textscFedUCB, a multiagent private algorithm for both centralized and decentralized (peer-to-peer) federated learning. We provide a rigorous technical analysis of its utility in terms of regret, improving several results in cooperative bandit learning, and provide rigorous privacy guarantees as well. Our algorithms provide competitive performance both in terms of pseudoregret bounds and empirical benchmark performance in various multi-agent settings.