Fast-Adapting Environment-Agnostic Device-Free Indoor Localization via Federated Meta-Learning

Bing Jia Chen, Ronald Y. Chang, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Deep learning-based device-free fingerprinting indoor localization faces the challenge of high data-labeling and training costs, especially when localization is required in multiple environments. A general model that can adapt to multiple environments and reduce these costs while maintaining data privacy is highly desirable. This paper proposes a federated meta-learning framework for device-free indoor localization, where each client, representing an environment or task, collaboratively train a general environment-agnostic model while preserving their data privacy. Fast adaptation to new environments is achieved by downloading the general model from the server and updating the model locally with only few labeled data. The proposed system is applicable to heterogeneous environments with varying layouts, dimensions, or numbers of locations. Real-world experiments demonstrate the effectiveness of the proposed method and its potential for significant data-labeling and training cost reductions.

Original languageEnglish (US)
Title of host publicationICC 2023 - IEEE International Conference on Communications
Subtitle of host publicationSustainable Communications for Renaissance
EditorsMichele Zorzi, Meixia Tao, Walid Saad
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538674628
StatePublished - 2023
Externally publishedYes
Event2023 IEEE International Conference on Communications, ICC 2023 - Rome, Italy
Duration: May 28 2023Jun 1 2023

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


Conference2023 IEEE International Conference on Communications, ICC 2023

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


  • Indoor localization
  • channel state information (CSI)
  • federated meta-learning
  • fingerprinting
  • graph neural network (GNN)


Dive into the research topics of 'Fast-Adapting Environment-Agnostic Device-Free Indoor Localization via Federated Meta-Learning'. Together they form a unique fingerprint.

Cite this