Reliable customer analysis using federated learning and exploring deep-attention edge intelligence
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The Internet of Things (IoT) and smart cities are flourishing with distributed systems in mobile wireless networks. As a result, an enormous amount of data are being generated for devices at the network edge. This results in privacy concerns, sensor data management issues, and data utilization issues. In this research, we propose a collaborative clustering method where the exchange of raw data is not required. The attention-based model used with a federated learning framework. The edge devices compute the model updates using local data and send them to the server for aggregation. Repetition is performed in multiple rounds until a convergence point reached. The transaction data used to train the attention model that gives a low dimensional embedding. Afterwards, we share the convergence model among the client/stores. Then, efficient pattern mining methods known as a clustering-based dynamic method (CBDM) are applied. For experimentation, we used retail store data to cluster the customer based on purchase behaviour. The proposed clustering method used semantic embedding to extract and then cluster them by discovering relevant patterns. The method achieved the 0.75 ROC values for the random distribution and 0.70 for the fixed distribution. The clustering method can help to reduce communication costs while ensuring privacy.
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Utilizing FL in IoT data scenarios avoids data storage, distributes data among multiple clients, and ensures individual data security through local training.
“The benefit of utilizing FL in this scenario is that the data storing mechanism is avoided and distributed among multiple clients, and because the data is trained locally on each client, the security of individual data is ensured [240].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [240]
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- Authors
- Ahmed, U., Srivastava, G., & Lin, J. C.-W.
- Affiliations
- Western Norway University of Applied Sciences (NO)
- China Medical University (TW)
- Corresponding author
- Lin, Jerry Chun-Wei
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2022-02
- Volume
- 127
- Pages
- 70-79
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W3197507259
- Cited by
- 52
- References
- 46
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 10 · 2023: 14 · 2022: 13
- FWCI
- 3.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Future Generation Computer Systems (publishedVersion, cc-by)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Keywords
- federated learning, attention-based models, edge computing, customer segmentation, purchase behaviour, privacy-preserving data analysis, collaborative clustering, semantic embeddings, transaction data, retail stores, communication cost reduction, Internet of Things
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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