Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning | Paper Citation Record

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This paper cites A survey on distributed machine learning.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning A survey on distributed machine learning

Reference 1

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This paper cites Neurosurgeon: Collaborative intelligence between the cloud and mobile edge.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Neurosurgeon: Collaborative intelligence between the cloud and mobile edge

Reference 2

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Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Unresolved cited work

Reference 3

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This paper cites Edge-host partitioning of deep neural networks with feature space encoding for resource-constrained internet-of-things platforms.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Edge-host partitioning of deep neural networks with feature space encoding for resource-constrained internet-of-things platforms

Reference 4

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Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Unresolved cited work

Reference 5

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This paper cites Evasion attacks against machine learning at test time.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Evasion attacks against machine learning at test time

Reference 6

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This paper cites Intriguing properties of neural networks.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Intriguing properties of neural networks

Reference 7

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This paper cites Practical black-box attacks against machine learning.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Practical black-box attacks against machine learning

Reference 8

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This paper cites Lord, Romain Mueller, and Luca Bertinetto.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Lord, Romain Mueller, and Luca Bertinetto

Reference 9

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This paper cites Diversitycanbetransferred: Output diversification for white-and black-box attacks.Advances in neural information processing systems, 33:4536–4548, 2020.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Diversitycanbetransferred: Output diversification for white-and black-box attacks.Advances in neural information processing systems, 33:4536–4548, 2020

Reference 10

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This paper cites Robust and Privacy-Preserving Collaborative Learning: A Comprehensive Survey.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Robust and Privacy-Preserving Collaborative Learning: A Comprehensive Survey

Reference 11

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This paper cites Security Implications of Edge Computing in Cloud Networks.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Security Implications of Edge Computing in Cloud Networks

Reference 12

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This paper cites Towards deep learning models resistant to adversarial attacks.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Towards deep learning models resistant to adversarial attacks

Reference 13

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This paper cites Jordan, and Ion Stoica.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Jordan, and Ion Stoica

Reference 14

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This paper cites Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters

Reference 15

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This paper cites A comprehensive survey on iot attacks: Taxonomy, detection mecha- nisms and challenges.Journal of Information and Intelligence, 2(6):455–513, 2024.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning A comprehensive survey on iot attacks: Taxonomy, detection mecha- nisms and challenges.Journal of Information and Intelligence, 2(6):455–513, 2024

Reference 16

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This paper cites A survey on iot security: Vulnerability detection and protection.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning A survey on iot security: Vulnerability detection and protection

Reference 17

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Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Unresolved cited work

Reference 18

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This paper cites Iot botnet forensics: A comprehensive digital forensic case study on mirai botnet servers.Forensic Science International: Digital Investigation, 32:300926, 2020.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Iot botnet forensics: A comprehensive digital forensic case study on mirai botnet servers.Forensic Science International: Digital Investigation, 32:300926, 2020

Reference 19

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This paper cites A survey of electromagnetic side-channel attacks and discussion on their case-progressing potential for digital forensics.Digital Investigation, 29:43–54, 2019.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning A survey of electromagnetic side-channel attacks and discussion on their case-progressing potential for digital forensics.Digital Investigation, 29:43–54, 2019

Reference 20

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This paper cites Privacy and robustness in federated learning: 23 Attacks and defenses.IEEE transactions on neural networks and learning systems, 2022.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Privacy and robustness in federated learning: 23 Attacks and defenses.IEEE transactions on neural networks and learning systems, 2022

Reference 21

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This paper cites Backdoor attacks and defenses in feature-partitioned collaborative learning.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Backdoor attacks and defenses in feature-partitioned collaborative learning

Reference 22

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This paper cites Dba: Distributed backdoor attacks against federated learning.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Dba: Distributed backdoor attacks against federated learning

Reference 23

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This paper cites Edge-only universal adversarial attacks in distributed learning.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Edge-only universal adversarial attacks in distributed learning

Reference 24

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Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Unresolved cited work

Reference 25

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This paper cites Goodfellow, Jonathon Shlens, and Christian Szegedy.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Goodfellow, Jonathon Shlens, and Christian Szegedy

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:52:35.541109Z

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This paper cites On the minimal adversarial perturbation for deep neural networks with provable estimation error.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning On the minimal adversarial perturbation for deep neural networks with provable estimation error

Reference 27

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This paper cites Black-box adversarial attacks with limited queries and information.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Black-box adversarial attacks with limited queries and information

Reference 28

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This paper cites Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors

Reference 29

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This paper cites Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models

Reference 30

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This paper cites Simple black-box adversarial attacks.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Simple black-box adversarial attacks

Reference 31

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This paper cites Im- proving black-box adversarial attacks with a transfer-based prior.Advances in neural information processing systems, 32, 2019.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Im- proving black-box adversarial attacks with a transfer-based prior.Advances in neural information processing systems, 32, 2019

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This paper cites Why do adver- sarial attacks transfer? explaining transferability of evasion and poisoning 24 attacks.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Why do adver- sarial attacks transfer? explaining transferability of evasion and poisoning 24 attacks

Reference 33

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This paper cites A Survey on Transferability of Adversarial Examples across Deep Neural Networks.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning A Survey on Transferability of Adversarial Examples across Deep Neural Networks

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This paper cites A review of black-box adversarial attacks on image classification.Neurocomputing, 610:128512, 2024.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning A review of black-box adversarial attacks on image classification.Neurocomputing, 610:128512, 2024

Reference 35

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This paper cites Blackbox attacks via surrogate ensemble search.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Blackbox attacks via surrogate ensemble search

Reference 36

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Observation ee4a79b3-f122-4b15-bf8e-c782c1f6ec7c · outbound

This paper cites Training meta-surrogate model for transferable adversarial attack.Proceedings of the AAAI Conference on Artificial Intelligence, 37(8):9516–9524, Jun.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Training meta-surrogate model for transferable adversarial attack.Proceedings of the AAAI Conference on Artificial Intelligence, 37(8):9516–9524, Jun

Reference 37

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This paper cites Stealing machine learning models via prediction{APIs}.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Stealing machine learning models via prediction{APIs}

Reference 38

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This paper cites I know what you trained last summer: A survey on stealing machine learning models and defences.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning I know what you trained last summer: A survey on stealing machine learning models and defences

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Observation 925a1cb5-7ce6-43b5-8e74-8e287d2ecfeb · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Distilling the Knowledge in a Neural Network

Reference 40

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This paper cites A Survey on Knowledge Distillation of Large Language Models.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning A Survey on Knowledge Distillation of Large Language Models

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This paper cites A systematic evaluation of transient execution attacks and defenses.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning A systematic evaluation of transient execution attacks and defenses

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This paper cites Chang, Ching-Hsien Hsu, and Shangguang Wang.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Chang, Ching-Hsien Hsu, and Shangguang Wang

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Observation 46eeb3d4-d37b-4736-ac27-5bdc70c09f4e · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning The cityscapes dataset for semantic urban scene understanding

Reference 44

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This paper cites A comprehensive overhaul of feature distillation.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning A comprehensive overhaul of feature distillation

Reference 45

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This paper cites Cifar-10 (canadian institute for advanced research).

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Cifar-10 (canadian institute for advanced research)

Reference 46

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This paper cites Split computing and early exiting for deep learning applications: Survey and research challenges.ACM Computing Surveys, 55(5):1–30, 2022.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Split computing and early exiting for deep learning applications: Survey and research challenges.ACM Computing Surveys, 55(5):1–30, 2022

Reference 47

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 48

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This paper cites Deep residual learning for image recognition.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Deep residual learning for image recognition

Reference 49

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This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 50

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Observation c3d85eff-8f02-4cbf-82f0-7ae686d875bb · outbound

This paper cites Subspace attack: Exploiting promising subspaces for query-efficient black-box attacks.

Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning Subspace attack: Exploiting promising subspaces for query-efficient black-box attacks

Reference 51

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