Search Results for author:
Found 3 papers, 3 papers with code
Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things
Federated learning (FL) and split learning (SL) are state-of-the-art distributed machine learning techniques to enable machine learning training without accessing raw data on clients or end devices.
End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things
For learning performance, which is specified by the model accuracy and convergence speed metrics, we empirically evaluate both FL and SplitNN under different types of data distributions such as imbalanced and non-independent and identically distributed (non-IID) data.
Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?
We observed that the 1D CNN model under split learning can achieve the same accuracy of 98. 9\% like the original (non-split) model.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
