Fast-Adapting Environment-Agnostic Device-Free Indoor Localization via Federated Meta-Learning
Author:
Affiliation:
1. Academia Sinica,Research Center for Information Technology Innovation,Taiwan
2. Princeton University,Department of Electrical and Computer Engineering,USA
Funder
U.S. National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10278505/10278554/10278802.pdf?arnumber=10278802
Reference18 articles.
1. FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing
2. A Privacy-Preserved Online Personalized Federated Learning Framework for Indoor Localization
3. Personalized federated learning: A meta-learning approach;fallah;ArXiv,2020
4. Federated meta-learning with fast convergence and efficient communication;chen;ArXiv,2018
5. Personalized Federated Learning over non-IID Data for Indoor Localization
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. SALLoc: An Accurate Target Localization in WiFi-Enabled Indoor Environments via SAE-ALSTM;IEEE Access;2024
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