Federated Learning for Indoor Localization via Model Reliability With Dropout

Author:

Park Junha1,Moon Jiseon1ORCID,Kim Taekyoon1,Wu Peng2,Imbiriba Tales2,Closas Pau2ORCID,Kim Sunwoo1ORCID

Affiliation:

1. Department of Electronic Engineering, Hanyang University, Seoul, South Korea

2. Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USA

Funder

Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea governmen

Developed of wireless communication tracking based location information system in disaster scene for fire-fighters and person who requested rescue)

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Modeling and Simulation

Cited by 15 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. Model Self-Supervised Federated Learning for Heterogeneous Fingerprint Localization;IEEE Wireless Communications Letters;2024-08

2. Secure Localization using Federated Learning and Blockchain: A Survey;2024 16th International Conference on Human System Interaction (HSI);2024-07-08

3. FGLNoRe: Federated Graph Learning on Non-IID RF Fingerprints for Building-Floor Positioning;2024 IEEE 14th International Conference on Electronics Information and Emergency Communication (ICEIEC);2024-05-24

4. GWO-Boosted Multi-Attribute Client Selection for Over- The-Air Federated Learning;2024 20th International Conference on the Design of Reliable Communication Networks (DRCN);2024-05-06

5. A Three-Level Federated Learning Framework for CSI Fingerprint Based Indoor Localization in Multiple Servers Environment;IEEE Communications Letters;2024-04

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