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Enhancing Critical Infrastructure Cybersecurity: Collaborative Dnn Synthesis in the Cloud Continuum

https://doi.org/10.2139/ssrn.4871453
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/int.22992openalex-extracted
Security of federated learning for cloud‐edge intelligence collaborative computing
inferredcites →https://doi.org/10.1007/s11704-021-0598-zopenalex-extracted
Challenges and future directions of secure federated learning: a survey
inferredcites →https://doi.org/10.1016/j.asoc.2022.108439openalex-extracted
Cybersecurity of multi-cloud healthcare systems: A hierarchical deep learning approach
inferredcites →https://doi.org/10.1016/j.comnet.2023.109724openalex-extracted
Efficient intrusion detection toward IoT networks using cloud–edge collaboration
inferredcites →https://doi.org/10.1016/j.future.2019.05.041openalex-extracted
Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset
inferredcites →https://doi.org/10.1016/j.is.2021.101840openalex-extracted
The convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT)
inferredcites →https://doi.org/10.1109/access.2020.3029649openalex-extracted
A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing
inferredcites →https://doi.org/10.1109/access.2020.3030726openalex-extracted
Deep Learning Enabled Data Offloading With Cyber Attack Detection Model in Mobile Edge Computing Systems
inferredcites →https://doi.org/10.1109/ic2e.2018.00042openalex-extracted
Cloud-Based or On-Device: An Empirical Study of Mobile Deep Inference
inferredcites →https://doi.org/10.1109/icassp43922.2022.9746925openalex-extracted
Federated Learning Challenges and Opportunities: An Outlook
inferredcites →https://doi.org/10.1109/iraset52964.2022.9738045openalex-extracted
A Stratified IoT Deep Learning based Intrusion Detection System
inferredcites →https://doi.org/10.1109/jiot.2021.3051158openalex-extracted
Digital Twin for Intelligent Context-Aware IoT Healthcare Systems
inferredcites →https://doi.org/10.1109/jsac.2019.2904348openalex-extracted
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
inferredcites →https://doi.org/10.1109/milcis.2015.7348942openalex-extracted
UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)
inferredcites →https://doi.org/10.1109/mnet.2023.3321923openalex-extracted
An Intelligent Edge-Cloud Collaborative Framework for Communication Security in Distributed Cyber-Physical Systems
inferredcites →https://doi.org/10.1109/tii.2022.3147831openalex-extracted
Edge-Based Collaborative Training System for Artificial Intelligence-of-Things
inferredcites →https://doi.org/10.1109/tmc.2020.3045266openalex-extracted
FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring
inferredcites →https://doi.org/10.1109/tmc.2021.3114193openalex-extracted
Task Partitioning and Offloading in DNN-Task Enabled Mobile Edge Computing Networks
inferredcites →https://doi.org/10.1109/tnet.2023.3271674openalex-extracted
Dynamic Service Provisioning in the Edge-Cloud Continuum With Bounded Resources
inferredcites →https://doi.org/10.1109/wf-iot54382.2022.10152292openalex-extracted
UNITE: Integrated IoT-Edge-Cloud Continuum
inferredcites →https://doi.org/10.1145/3447548.3467078openalex-extracted
Auto-Split: A General Framework of Collaborative Edge-Cloud AI
inferredcites →https://doi.org/10.1155/2022/4016073openalex-extracted
Deep Learning for Intrusion Detection and Security of Internet of Things (IoT): Current Analysis, Challenges, and Possible Solutions
inferredcites →https://doi.org/10.1155/2022/9726286openalex-extracted
Deep Learning Distributed Architecture Design Implementation for Computer Vision
inferredcites →https://doi.org/10.1162/99608f92.a42024d0openalex-extracted
Statistics and Data Science for Cybersecurity
inferredcites →https://doi.org/10.1186/s13677-022-00377-4openalex-extracted
Federated learning in cloud-edge collaborative architecture: key technologies, applications and challenges
inferredcites →https://doi.org/10.1186/s13677-023-00477-9openalex-extracted
An intelligent approach of task offloading for dependent services in Mobile Edge Computing
inferredcites →https://doi.org/10.1613/jair.1.13547openalex-extracted
The Computational Complexity of ReLU Network Training Parameterized by Data Dimensionality
inferredcites →https://doi.org/10.23919/fruct54823.2022.9770931openalex-extracted
Artificial Intelligence in the IoT Era: A Review of Edge AI Hardware and Software
inferredcites →https://doi.org/10.3390/electronics11142255openalex-extracted
A Cloud-Edge-Smart IoT Architecture for Speeding Up the Deployment of Neural Network Models with Transfer Learning Techniques
inferredcites →https://doi.org/10.3390/electronics12041027openalex-extracted
Machine-Learning-Based IoT–Edge Computing Healthcare Solutions
inferredcites →https://doi.org/10.3390/fi11060127openalex-extracted
Cyber-Storms Come from Clouds: Security of Cloud Computing in the IoT Era
inferredcites →https://doi.org/10.3390/s22020450openalex-extracted
Federated Learning in Edge Computing: A Systematic Survey
inferredcites →https://doi.org/10.3390/s22155866openalex-extracted
Smart Strawberry Farming Using Edge Computing and IoT
inferredcites →https://doi.org/10.3390/s22228784openalex-extracted
A Design and Application of Municipal Service Platform Based on Cloud-Edge Collaboration for Smart Cities
inferredcites →https://doi.org/10.3390/s23177358openalex-extracted
Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey
inferredcites →https://doi.org/10.3390/smartcities4030064openalex-extracted
Cloud-Based IoT Applications and Their Roles in Smart Cities
inferredcites →https://doi.org/10.48550/arxiv.1603.02836openalex-extracted
Faster learning of deep stacked autoencoders on multi-core systems using synchronized layer-wise pre-training
inferredcites →https://doi.org/10.48550/arxiv.2003.12172openalex-extracted
Edge Intelligence: Architectures, Challenges, and Applications
inferredcites →https://doi.org/10.48550/arxiv.2111.03972openalex-extracted
Understanding Layer-wise Contributions in Deep Neural Networks through Spectral Analysis
inferredcites →https://doi.org/10.48550/arxiv.2304.10020openalex-extracted
A Survey on Deep Neural Network Partition over Cloud, Edge and End Devices
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