1 citations · 2 across the 6 of their papers we have counts for
6 papers
Encoded Spatial Attribute in Multi-Tier Federated Learning
Asfia Kawnine, Francis Palma, Seyed Alireza Rahimi Azghadi +1
This research presents an Encoded Spatial Multi-Tier Federated Learning approach for a comprehensive evaluation of aggregated models for geospatial data. In the client tier, encodi…
An Adaptive Indoor Localization Approach Using WiFi RSSI Fingerprinting with SLAM-Enabled Robotic Platform and Deep Neural Networks
Seyed Alireza Rahimi Azghadi, Atah Nuh Mih, Asfia Kawnine +3
Indoor localization plays a vital role in the era of the IoT and robotics, with WiFi technology being a prominent choice due to its ubiquity. We present a method for creating WiFi…
Achieving Pareto Optimality using Efficient Parameter Reduction for DNNs in Resource-Constrained Edge Environment
Atah Nuh Mih, Alireza Rahimi, Asfia Kawnine +4
This paper proposes an optimization of an existing Deep Neural Network (DNN) that improves its hardware utilization and facilitates on-device training for resource-constrained edge…
Developing a Resource-Constraint EdgeAI model for Surface Defect Detection
Atah Nuh Mih, Hung Cao, Asfia Kawnine +1
Resource constraints have restricted several EdgeAI applications to machine learning inference approaches, where models are trained on the cloud and deployed to the edge device. Th…
Evaluating Multi-Global Server Architecture for Federated Learning
Asfia Kawnine, Hung Cao, Atah Nuh Mih +1
Federated learning (FL) with a single global server framework is currently a popular approach for training machine learning models on decentralized environment, such as mobile devi…
ECAvg: An Edge-Cloud Collaborative Learning Approach using Averaged Weights
Atah Nuh Mih, Hung Cao, Asfia Kawnine +1
The use of edge devices together with cloud provides a collaborative relationship between both classes of devices where one complements the shortcomings of the other. Resource-cons…