# Federated Model Search via Reinforcement Learning
> FedRLNAS is an early federated neural architecture search method using reinforcement learning to let distributed clients collaboratively discover model architectures without sharing raw data.
Type: paper
Venue: ICDCS 2021
Date: 2021
Authors: Dixi Yao, Lingdong Wang, Jiayu Xu, Liyao Xiang, Shuo Shao, Yingqi Chen, Yanjun Tong
## Direct Links
- [Canonical topic page](https://dixiyao.github.io/topics/fedrlnas/)
- [Paper](https://ieeexplore.ieee.org/document/9546522)
- [PDF](https://dixiyao.github.io/assests/papers/Federated_Model_Search_via_Reinforcement_Learning.pdf)
- [Code](https://github.com/TL-System/plato/tree/main/examples/model_search/fedrlnas)
- [Slides](https://dixiyao.github.io/assests/slides/FedNas.pdf)
## Search Queries and Aliases
- FedRLNAS
- federated model search
- federated neural architecture search
- federated learning NAS
- federated learning + NAS
- NAS in federated learning
- reinforcement learning NAS federated
## Search Intents
- federated learning NAS code
- FedRLNAS paper
- federated model search reinforcement learning
- neural architecture search in federated learning