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Deep learning models for cloud, edge, fog, and IoT computing paradigms: Survey, recent advances, and future directions
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“A poisoning attack on a model utilized for initial-stage disease diagnosis could modify the prediction edge for certain conditions, which is key to false-negative or -positive inpatient diagnoses [77,78].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [78]
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Metadata
- Authors
- Ahmad, S., Shakeel, I., Mehfuz, S., & Ahmad, J.
- Affiliations
- Jamia Hamdard (IN)
- Jamia Millia Islamia (IN)
- Corresponding author
- Ahmad, Shahnawaz
- Venue
- Computer Science Review
- Publisher
- Elsevier BV
- Published
- 2023-08
- Volume
- 49
- Pages
- 100568
- ISSN
- 1574-0137 (ISSN-L)
- OpenAlex
- W4378190897
- Cited by
- 197
- References
- 494
- Citations by year
- 2026: 45 · 2025: 73 · 2024: 67 · 2023: 12
- FWCI
- 20.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computer Science Review (publishedVersion)
- Funders
- Jamia Millia Islamia
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- deep learning, cloud computing, edge computing, fog computing, Internet of Things, latency reduction, energy consumption, resource management, privacy, open problems
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