# Learning with Limited Shared Information in Multi-agent Multi-armed Bandit

arXiv: https://arxiv.org/abs/2502.15338 · HTML: https://mlsanity.com/paper/2502.15338/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 44 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 22 of 22)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data](https://mlsanity.com/paper/1811.11479/) (2018)
- [Federated Multi-armed Bandits with Personalization](https://mlsanity.com/paper/2102.13101/) (2021)
- [Federated Multi-Armed Bandits](https://mlsanity.com/paper/2101.12204/) (2021)
- [Bayesian Incentive-Compatible Bandit Exploration](https://mlsanity.com/paper/1502.04147/) (2015)
- [Social Learning in Multi Agent Multi Armed Bandits](https://mlsanity.com/paper/1910.02100/) (2019)
- [Energy Demand Prediction with Federated Learning for Electric Vehicle Networks](https://mlsanity.com/paper/1909.00907/) (2019)
- [A Comprehensive Survey of Incentive Mechanism for Federated Learning](https://mlsanity.com/paper/2106.15406/) (2021)
- [Bayesian Exploration: Incentivizing Exploration in Bayesian Games](https://mlsanity.com/paper/1602.07570/) (2016)
- [Efficient Decentralized Deep Learning by Dynamic Model Averaging](https://mlsanity.com/paper/1807.03210/) (2018)
- [Multi-Agent Multi-Armed Bandits with Limited Communication](https://mlsanity.com/paper/2102.08462/) (2021)
- [Incentivizing Exploration with Selective Data Disclosure](https://mlsanity.com/paper/1811.06026/) (2018)
- [Fair Exploration via Axiomatic Bargaining](https://mlsanity.com/paper/2106.02553/) (2021)
- [Robust Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2007.03812/) (2020)
- [Player-optimal Stable Regret for Bandit Learning in Matching Markets](https://mlsanity.com/paper/2307.10890/) (2023)
- [Multi-armed Bandits with Compensation](https://mlsanity.com/paper/1811.01715/) (2018)
- [When to Call Your Neighbor? Strategic Communication in Cooperative Stochastic Bandits](https://mlsanity.com/paper/2110.04396/) (2021)
- [Incentivizing Exploration with Heterogeneous Value of Money](https://mlsanity.com/paper/1512.08427/) (2015)
- [Incentivizing Exploration in Linear Bandits under Information Gap](https://mlsanity.com/paper/2104.03860/) (2021)
- [Bayesian Persuasion under Ex Ante and Ex Post Constraints](https://mlsanity.com/paper/2012.03272/) (2020)

## Abstract (arXiv)

Multi-agent multi-armed bandit (MAMAB) is a classic collaborative learning model and has gained much attention in recent years. However, existing studies do not consider the case where an agent may refuse to share all her information with others, e.g., when some of the data contains personal privacy. In this paper, we propose a novel limited shared information multi-agent multi-armed bandit (LSI-MAMAB) model in which each agent only shares the information that she is willing to share, and propose the Balanced-ETC algorithm to help multiple agents collaborate efficiently with limited shared information. Our analysis shows that Balanced-ETC is asymptotically optimal and its average regret (on each agent) approaches a constant when there are sufficient agents involved. Moreover, to encourage agents to participate in this collaborative learning, an incentive mechanism is proposed to make sure each agent can benefit from the collaboration system. Finally, we present experimental results to validate our theoretical results.