# Locally Private Distributed Reinforcement Learning

arXiv: https://arxiv.org/abs/2001.11718 · HTML: https://mlsanity.com/paper/2001.11718/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2020.

## Built on by (most cited first, 9 of 9)

- [Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability](https://mlsanity.com/paper/2209.08025/) (2022)
- [Differentially Private Regret Minimization in Episodic Markov Decision Processes](https://mlsanity.com/paper/2112.10599/) (2021)
- [Differentially Private Reinforcement Learning with Linear Function Approximation](https://mlsanity.com/paper/2201.07052/) (2022)
- [Offline Reinforcement Learning with Differential Privacy](https://mlsanity.com/paper/2206.00810/) (2022)
- [ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning](https://mlsanity.com/paper/2309.03081/) (2023)
- [Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions](https://mlsanity.com/paper/2209.04022/) (2022)
- [Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning](https://mlsanity.com/paper/2505.20621/) (2025)
- [Differentially Private Reinforcement Learning with Self-Play](https://mlsanity.com/paper/2404.07559/) (2024)
- [Private Federated Submodel Learning with Sparsification](https://mlsanity.com/paper/2205.15992/) (2022)

## Abstract (arXiv)

We study locally differentially private algorithms for reinforcement learning to obtain a robust policy that performs well across distributed private environments. Our algorithm protects the information of local agents' models from being exploited by adversarial reverse engineering. Since a local policy is strongly being affected by the individual environment, the output of the agent may release the private information unconsciously. In our proposed algorithm, local agents update the model in their environments and report noisy gradients designed to satisfy local differential privacy (LDP) that gives a rigorous local privacy guarantee. By utilizing a set of reported noisy gradients, a central aggregator updates its model and delivers it to different local agents. In our empirical evaluation, we demonstrate how our method performs well under LDP. To the best of our knowledge, this is the first work that actualizes distributed reinforcement learning under LDP. This work enables us to obtain a robust agent that performs well across distributed private environments.