SuzukiWest / CIFAR-CentralBigTransfer

This project aimed to determine the ideal hyperparameters to classify the CIFAR-100 dataset.

Using Transfer Learning in Building Federated Learning Models on Edge Devices

Senior Research Project - Completed at Mount Royal University - By Jordan Suzuki

Credit to Yasaman Amannejad & Saba F. Lameh for the help with the code and paper

Summary

This project aimed to determine the ideal hyperparameters to classify the CIFAR-100 dataset.

In addition to Federated Learning (FL) Transfer Learning (TL) was used to pre-train base models.

These hyperparameters consist of:

1. The base model (TL).
2. The amount of clients involved (FL).
3. The number of classification labels.
4. Number of model training rounds.
5. Constant vs. Variable client selection.