Search Results for author:
Found 5 papers, 0 papers with code
Achieving Pareto Optimality using Efficient Parameter Reduction for DNNs in Resource-Constrained Edge Environment
The results of the Caltech-101 image classification show that our model has a better test accuracy (76. 21%) than Xception (75. 89%), uses less memory on average (847. 9MB) than Xception (874. 6MB), and has faster training and inference times.
Developing a Resource-Constraint EdgeAI model for Surface Defect Detection
The results of our experiment show that our model has a remarkable performance with a test accuracy of 73. 45% without pre-training.
Evaluating Multi-Global Server Architecture for Federated Learning
We posit that implementing multiple global servers in federated learning can enhance efficiency by capitalizing on local collaborations and aggregating knowledge, and the error tolerance in regard to communication failure in the single server framework would be handled.
ECAvg: An Edge-Cloud Collaborative Learning Approach using Averaged Weights
The local (edge) models are then updated with the weights of the global (server) model.
TransferD2: Automated Defect Detection Approach in Smart Manufacturing using Transfer Learning Techniques
Our proposed approach can be applied in defect detection applications where insufficient data is available for training a model and can be extended to identify imperfections in new unseen data.
