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Found 8 papers, 1 papers with code
An Intrusion Detection System based on Deep Belief Networks
The CICIDS2017 dataset was used to train and evaluate the performance of our proposed DBN approach.
Ranked #3 on
Network Intrusion Detection
on CICIDS2017
A Dataset of Images of Public Streetlights with Operational Monitoring using Computer Vision Techniques
Each UMBRELLA node is installed on the pole of a lamppost and is equipped with a Raspberry Pi Camera Module v1 facing upwards towards the sky and lamppost light bulb.
A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challenges
Data-enabled cities are recently accelerated and enhanced with automated learning for improved Smart Cities applications.
LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices
Our framework is also generalisable, adapting to new sensor streams and environments with minimal online reconfiguration.
Sustainable Edge Intelligence Through Energy-Aware Early Exiting
However, due to their computational complexity, DL models consume significant amounts of energy, which can rapidly drain the battery and compromise the performance of IoT devices.
Federated Deep Learning for Intrusion Detection in IoT Networks
The comparison shows that the heterogeneous nature of the data has a considerable negative impact on the model's performance when trained in a distributed manner.
FROST: Towards Energy-efficient AI-on-5G Platforms -- A GPU Power Capping Evaluation
The Open Radio Access Network (O-RAN) is a burgeoning market with projected growth in the upcoming years.
Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems
We evaluate our approach within an AFL deployment consisting of 10 simulated clients with heterogeneous compute constraints and non-IID data.
