# Decentralized Blockchain-based Robust Multi-agent Multi-armed Bandit

arXiv: https://arxiv.org/abs/2402.04417 · HTML: https://mlsanity.com/paper/2402.04417/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 48 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 18 of 18)

- [Federated Linear Contextual Bandits](https://mlsanity.com/paper/2110.14177/) (2021)
- [Federated Bandit: A Gossiping Approach](https://mlsanity.com/paper/2010.12763/) (2020)
- [Distributed Cooperative Decision-Making in Multiarmed Bandits: Frequentist and Bayesian Algorithms](https://mlsanity.com/paper/1606.00911/) (2016)
- [Distributed Cooperative Decision Making in Multi-agent Multi-armed Bandits](https://mlsanity.com/paper/2003.01312/) (2020)
- [On Distributed Cooperative Decision-Making in Multiarmed Bandits](https://mlsanity.com/paper/1512.06888/) (2015)
- [Multi-Agent Multi-Armed Bandits with Limited Communication](https://mlsanity.com/paper/2102.08462/) (2021)
- [Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits](https://mlsanity.com/paper/2106.02575/) (2021)
- [Robust Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2007.03812/) (2020)
- [Exploiting Heterogeneity in Robust Federated Best-Arm Identification](https://mlsanity.com/paper/2109.05700/) (2021)
- [Near-Optimal Collaborative Learning in Bandits](https://mlsanity.com/paper/2206.00121/) (2022)
- [Multitask Bandit Learning Through Heterogeneous Feedback Aggregation](https://mlsanity.com/paper/2010.15390/) (2020)
- [Decentralized Randomly Distributed Multi-agent Multi-armed Bandit with Heterogeneous Rewards](https://mlsanity.com/paper/2306.05579/) (2023)
- [Private and Byzantine-Proof Cooperative Decision-Making](https://mlsanity.com/paper/2205.14174/) (2022)
- [Byzantine-Robust Distributed Online Learning: Taming Adversarial Participants in An Adversarial Environment](https://mlsanity.com/paper/2307.07980/) (2023)
- [A comprehensive survey on smart contract construction and execution: paradigms, tools, and systems](https://mlsanity.com/paper/2008.13413/) (2020)
- [Byzantine-Resilient Decentralized Multi-Armed Bandits](https://mlsanity.com/paper/2310.07320/) (2023)
- [Adding Concurrency to Smart Contracts](https://mlsanity.com/paper/1702.04467/) (2017)
- [Regret Lower Bounds in Multi-agent Multi-armed Bandit](https://mlsanity.com/paper/2308.08046/) (2023)

## Built on by (most cited first, 1 of 1)

- [Bandit Learning in General Open Multi-agent Systems](https://mlsanity.com/paper/2605.06202/) (2026)

## Abstract (arXiv)

We study a robust, i.e. in presence of malicious participants, multi-agent multi-armed bandit problem where multiple participants are distributed on a fully decentralized blockchain, with the possibility of some being malicious. The rewards of arms are homogeneous among the honest participants, following time-invariant stochastic distributions, which are revealed to the participants only when certain conditions are met to ensure that the coordination mechanism is secure enough. The coordination mechanism's objective is to efficiently ensure the cumulative rewards gained by the honest participants are maximized. To this end, we are the first to incorporate advanced techniques from blockchains, as well as novel mechanisms, into such a cooperative decision making framework to design optimal strategies for honest participants. This framework allows various malicious behaviors and the maintenance of security and participant privacy. More specifically, we select a pool of validators who communicate to all participants, design a new consensus mechanism based on digital signatures for these validators, invent a UCB-based strategy that requires less information from participants through secure multi-party computation, and design the chain-participant interaction and an incentive mechanism to encourage participants' participation. Notably, we are the first to prove the theoretical regret of the proposed algorithm and claim its optimality. Unlike existing work that integrates blockchains with learning problems such as federated learning which mainly focuses on optimality via computational experiments, we demonstrate that the regret of honest participants is upper bounded by logT under certain assumptions. The regret bound is consistent with the multi-agent multi-armed bandit problem, both without malicious participants and with purely Byzantine attacks which do not affect the entire system.