FMFTC

This repository contains the code used in our paper: FMFTC: Federated Multi Feature Trajectory Clustering

Requirements

• Ubuntu OS
• Python >= 3.7 (Anaconda3 is recommended)
• PyTorch 1.12+
• A Nvidia GPU with cuda 10.2+

Please refer to the source code to install all required packages in Python.

Data

• Our qstaxi trajectory clustering datasets are stored in data according to our Ground Truth Generation algorithm.
• We provide raw trajectory data for training.

Preprocessing

The preprocessing step will generate all data required in the training stage.

For the qstaxi dataset, you can do as follows.

cd Preprocess
python preprocess.py
python spatial_similarity.py
python speed_similarity.py
python temporal_similarity.py
python merge_STD_similarity.py
cd ..

Train

1. Training with parameters

python main.py

2. The training produces two model coordinator_checkpoint.pkl, participant_checkpoint.pkl and coordinator_NMI_BEST.pkl, participant_NMI_BEST.pkl. checkpoint contains the latest trained model and NMI_BEST saves the model which has the best performance on the validation data.

Some code comes from ST2vec.