# Federated Recommendation System via Differential Privacy

arXiv: https://arxiv.org/abs/2005.06670 · HTML: https://mlsanity.com/paper/2005.06670/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2020.

## Built on by (most cited first, 26 of 26)

- [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)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits](https://mlsanity.com/paper/2207.03106/) (2022)
- [Federated Neural Bandits](https://mlsanity.com/paper/2205.14309/) (2022)
- [Poisoning Federated Recommender Systems with Fake Users](https://mlsanity.com/paper/2402.11637/) (2024)
- [Federated Linear Contextual Bandits with User-level Differential Privacy](https://mlsanity.com/paper/2306.05275/) (2023)
- [Privacy-Preserving Communication-Efficient Federated Multi-Armed Bandits](https://mlsanity.com/paper/2111.01570/) (2021)
- [A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy](https://mlsanity.com/paper/2405.13453/) (2024)
- [Collaborative Learning in Kernel-based Bandits for Distributed Users](https://mlsanity.com/paper/2207.07948/) (2022)
- [Federated X-Armed Bandit](https://mlsanity.com/paper/2205.15268/) (2022)
- [Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2408.14736/) (2024)
- [Gap-Dependent Bounds for Federated Q-learning](https://mlsanity.com/paper/2502.02859/) (2025)
- [Decentralized Upper Confidence Bound Algorithms for Homogeneous Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2111.10933/) (2021)
- [Federated Online Sparse Decision Making](https://mlsanity.com/paper/2202.13448/) (2022)
- [Byzantine-Resilient Decentralized Multi-Armed Bandits](https://mlsanity.com/paper/2310.07320/) (2023)
- [Federated Linear Bandits with Finite Adversarial Actions](https://mlsanity.com/paper/2311.00973/) (2023)
- [Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning](https://mlsanity.com/paper/2506.04626/) (2025)
- [Federated Learning for Heterogeneous Bandits with Unobserved Contexts](https://mlsanity.com/paper/2303.17043/) (2023)
- [Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups](https://mlsanity.com/paper/2409.19092/) (2024)
- [Vertical Federated Linear Contextual Bandits](https://mlsanity.com/paper/2210.11050/) (2022)
- [DRIFT: A Federated Recommender System with Implicit Feedback on the Items](https://mlsanity.com/paper/2304.09084/) (2023)
- [Reward Teaching for Federated Multi-armed Bandits](https://mlsanity.com/paper/2305.02441/) (2023)
- [Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces](https://mlsanity.com/paper/2311.01722/) (2023)
- [FastPFRec: A Fast Personalized Federated Recommendation with Secure Sharing](https://mlsanity.com/paper/2603.20283/) (2026)

## Abstract (arXiv)

In this paper, we are interested in what we term the federated private bandits framework, that combines differential privacy with multi-agent bandit learning. We explore how differential privacy based Upper Confidence Bound (UCB) methods can be applied to multi-agent environments, and in particular to federated learning environments both in `master-worker' and `fully decentralized' settings. We provide a theoretical analysis on the privacy and regret performance of the proposed methods and explore the tradeoffs between these two.