Securing Pseudo-Model Parallelism-Based Collaborative DNN Inference for Edge Devices Article Swipe
Adewale Adeyemo
,
Parth Patel
,
Syed Rafay Hasan
,
Mohammad Ashiqur Rahaman
,
Soamar Homsi
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3477293
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3477293
Collaborative Deep Neural Network Inference (CDNN) has emerged as one of the significant strategies for efficient and lightweight computation on resource-constrained devices (like drones), especially in the case of adverse events like natural disasters. Several strategies have been proposed in the implementation of collaborative inference. Notably, parllale CDNN (P-CDNN) emerges as a crucial strategy. In the context of P-CDNN, the CDNN effectively partitions and distributes input data across multiple drone devices, each equipped with pre-trained Deep Neural Network (DNN) models. However, this collaborative framework is vulnerable to several security concerns, especially when one or more devices are compromised. To address this challenge and enhance the robustness of CDNNs, specifically in drone applications, we propose an innovative solution that involves modification of P-CDNN (we called ins Pseudo-Model- Parallelism-based CDNN or PS-CDNN). We have also incorporated novel filters into the drone system to address attacks on intermediate data (feature maps). These filters are trained using multi-strength adversarial training techniques, employing adversarial intermediate data collected from collaborating drones. This reinforcement significantly strengthens CDNNs against potential adversarial attacks. We conducted comprehensive evaluations using two widely recognized benchmark datasets, state-of-the-art Convolutional Neural Network (CNN) models, and a collaborative setup to validate the effectiveness of our approach. These results showcase a remarkable average improvement of X in the top-1 accuracy of the model, highlighting the effectiveness and model-agnostic nature of our approach in drone applications. Furthermore, our approach exhibits exceptional adaptability to various DNN architectures while substantially bolstering the security of drone-based intelligence applications.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3477293
- OA Status
- gold
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4403277875Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3477293Digital Object Identifier
- Title
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Securing Pseudo-Model Parallelism-Based Collaborative DNN Inference for Edge DevicesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Adewale Adeyemo, Parth Patel, Syed Rafay Hasan, Mohammad Ashiqur Rahaman, Soamar HomsiList of authors in order
- Landing page
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https://doi.org/10.1109/access.2024.3477293Publisher landing page
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2024.3477293Direct OA link when available
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Computer science, Parallelism (grammar), Inference, Parallel computing, Enhanced Data Rates for GSM Evolution, Data parallelism, Theoretical computer science, Computer architecture, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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