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Found 15 papers, 1 papers with code
FedMap: Iterative Magnitude-Based Pruning for Communication-Efficient Federated Learning
This paper introduces FedMap, a novel method that aims to enhance the communication efficiency of FL deployments by collaboratively learning an increasingly sparse global model through iterative, unstructured pruning.
Computing Within Limits: An Empirical Study of Energy Consumption in ML Training and Inference
In this paper, we examine multiple models and hardware configurations to identify correlations across the various measurements and metrics and key contributors to energy reduction.
Multi-stage Attack Detection and Prediction Using Graph Neural Networks: An IoT Feasibility Study
With the ever-increasing reliance on digital networks for various aspects of modern life, ensuring their security has become a critical challenge.
Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the UMBRELLA IoT Testbed
UMBRELLA is a large-scale, open-access Internet of Things (IoT) ecosystem incorporating over 200 multi-sensor multi-wireless nodes, 20 collaborative robots, and edge-intelligence-enabled devices.
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.
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.
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.
FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments
In order to simultaneously reduce communication traffic and maintain the integrity of inference models, we introduce FLARE, a novel lightweight dual-scheduler FL framework that conditionally transfers training data, and deploys models between edge and sensor endpoints based on observing the model's training behaviour and inference statistics, respectively.
Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework
This paper presents LE3D; a novel data drift detection framework for preserving data integrity and confidentiality.
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.
Energy-Rate-Quality Tradeoffs of State-of-the-Art Video Codecs
The adoption of video conferencing and video communication services, accelerated by COVID-19, has driven a rapid increase in video data traffic.
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.
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.
Location Anomalies Detection for Connected and Autonomous Vehicles
Future Connected and Automated Vehicles (CAV), and more generally ITS, will form a highly interconnected system.
A Dataset of Full-Stack ITS-G5 DSRC Communications over Licensed and Unlicensed Bands Using a Large-Scale Urban Testbed
During each experimental session, for each transceiver, all the transmitted and received CAMs were recorded.
Networking and Internet Architecture
