Human-Activity-Recognition

Federated Learning based Classification

1. Created a Federated Learning-based XGB classifier model for Human activity recognition using Smartphones dataset.
2. Divided the dataset into private and public parts and trained private individual models. Using these private individual models weights, the final public model was trained to ensure the security of the private dataset.
3. The model achieved an overall accuracy of 93%.