Privacy-Preserving AI on Edge Devices: A Comparative Study of On-Device Learning and Cloud Processing Article Swipe
The rise of edge computing has transformed how artificial intelligence (AI) processes personal and sensitive data. Traditional cloud-based AI architectures raise privacy concerns due to the transmission and centralized storage of user data. On-device learning, by contrast, enables local inference, enhancing data privacy and reducing network dependency. This study conducts a comparative analysis of on-device learning and cloud-based AI processing using a human activity recognition (HAR) task. We evaluate both approaches on model accuracy, latency, energy consumption, and privacy exposure. Our findings reveal that while cloud-based models slightly outperform in accuracy and energy efficiency, on-device models offer significantly lower latency and enhanced privacy by eliminating the need for data transmission. The results support a privacy-preserving edge AI paradigm, particularly for applications in healthcare, personal security, and mobile computing. Recommendations are provided to guide system architects in selecting deployment strategies aligned with privacy, performance, and regulatory requirements.
Related Topics
Concepts
Computer science
Software deployment
Cloud computing
Information privacy
Edge computing
Enhanced Data Rates for GSM Evolution
Edge device
Artificial intelligence
Mobile device
Deep learning
Latency (audio)
Machine learning
Data processing
Applications of artificial intelligence
Transmission (telecommunications)
Key (lock)
Data science
Artificial neural network
Differential privacy
Energy (signal processing)
Human–computer interaction
Data modeling
Efficient energy use
Big data
Data transmission
Energy consumption
Mobile computing
Human intelligence
Computer security
Data Protection Act 1998
Cellular network
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5281/zenodo.17829207
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7109047511
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7109047511Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.17829207Digital Object Identifier
- Title
-
Privacy-Preserving AI on Edge Devices: A Comparative Study of On-Device Learning and Cloud ProcessingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-12-05Full publication date if available
- Authors
-
Mission FranklinList of authors in order
- Landing page
-
https://doi.org/10.5281/zenodo.17829207Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5281/zenodo.17829207Direct OA link when available
- Concepts
-
Computer science, Software deployment, Cloud computing, Information privacy, Edge computing, Enhanced Data Rates for GSM Evolution, Edge device, Artificial intelligence, Mobile device, Deep learning, Latency (audio), Machine learning, Data processing, Applications of artificial intelligence, Transmission (telecommunications), Key (lock), Data science, Artificial neural network, Differential privacy, Energy (signal processing), Human–computer interaction, Data modeling, Efficient energy use, Big data, Data transmission, Energy consumption, Mobile computing, Human intelligence, Computer security, Data Protection Act 1998, Cellular networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.inference, | 39 |
| abstract_inverted_index.outperform | 88 |
| abstract_inverted_index.processing | 59 |
| abstract_inverted_index.regulatory | 144 |
| abstract_inverted_index.strategies | 138 |
| abstract_inverted_index.Traditional | 16 |
| abstract_inverted_index.centralized | 28 |
| abstract_inverted_index.cloud-based | 17, 57, 85 |
| abstract_inverted_index.comparative | 51 |
| abstract_inverted_index.dependency. | 46 |
| abstract_inverted_index.efficiency, | 93 |
| abstract_inverted_index.eliminating | 104 |
| abstract_inverted_index.healthcare, | 122 |
| abstract_inverted_index.recognition | 64 |
| abstract_inverted_index.transformed | 6 |
| abstract_inverted_index.applications | 120 |
| abstract_inverted_index.consumption, | 76 |
| abstract_inverted_index.intelligence | 9 |
| abstract_inverted_index.particularly | 118 |
| abstract_inverted_index.performance, | 142 |
| abstract_inverted_index.transmission | 26 |
| abstract_inverted_index.architectures | 19 |
| abstract_inverted_index.requirements. | 145 |
| abstract_inverted_index.significantly | 97 |
| abstract_inverted_index.transmission. | 109 |
| abstract_inverted_index.Recommendations | 128 |
| abstract_inverted_index.privacy-preserving | 114 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 1 |
| citation_normalized_percentile.value | 0.8356383 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |