The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Next - Location Prediction Using Federated Learning on a Blockchain
Abstract
Mobile devices are a rich source of sensitive location data. In this paper, we propose a method for harnessing this data to provide better location predictions without sacrificing the privacy of the users generating this data. To this end, we propose utilizing Federated Learning to train locally on a user's mobile device, while simultaneously identifying and combatting the possibility of bad actors or adversaries that may deliberately report problematic data to hurt the training process. Furthermore, we propose using a blockchain instead of a centralized server for the training process, to ensure that the process is secure.
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In the citing paper
“Similar to traffic flow prediction, the authors in [44] proposed a blockchain-integrated FL framework for destination prediction, addressing user and location privacy concerns.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [44]
In the citing paper
“Additionally, FL has been applied to destination prediction tasks, providing precise location services without exposing sensitive user data [44].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [44]
Citations
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Cited by
Sorted by citations
Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 9
“Additionally, managing cumulative privacy loss over multiple queries or iterations (the privacy budget) is crucial to maintaining robust privacy guarantees over time.”
Metadata
- Authors
- Halim, S. M., Khan, L., & Thuraisingham, B.
- Affiliations
- The University of Texas at Dallas (US)
- Venue
- 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)
- Publisher
- IEEE
- Published
- 2020-10
- Pages
- 244-250
- OpenAlex
- W3121241170
- Cited by
- 16
- References
- 5
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 4 · 2022: 3 · 2021: 3
- FWCI
- 0.95
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Cognitive Machine Intelligence (CogMI) (publishedVersion)
- Funders
- NSF (DMS-1737978,DGE-2039542,OAC-1828467,OAC-1931541,DGE-1906630)
- IBM faculty award (Research)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
- Keywords
- location prediction, federated learning, blockchain, privacy-preserving machine learning, mobile phone location data, adversarial data, decentralized learning
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