| Autor: |
Minsoo Jeong, Sang Won Choi, Sunwoo Kim |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
ICT Express, Vol 9, Iss 4, Pp 548-555 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2405-9595 |
| DOI: |
10.1016/j.icte.2023.01.009 |
| Popis: |
This paper presents a tutorial on Deep Learning (DL) with Federated Learning (FL)-based indoor localization method for non-Independently and Identically Distributed (non-IID) fingerprinting databases. To this end, this paper explains systematic approaches for addressing privacy concerns and performance degradation issues in non-IID fingerprinting databases. The method presented in this tutorial entails the application of a personalized layer, model reliability, and Layer-wise local model’s Weight Change (LWC) information to FL. This tutorial provides intuitions to be considered by future researchers to improve the performance of FL-based fingerprinting localization by summarizing the above-mentioned methods into three FL-based techniques: high-complexity training for performance improvement of local training models, exact characteristics of the local model for global model aggregation, and Bayesian data fusion for probabilistic clustering, to improve FL-based indoor localization performance. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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