Bell-Fintech/Federated-Time-Series-Forecasting

Federated Learning for 5G Base Station Traffic Forecasting

Federated Time-Series Forecasting

This is the code accompanying the submission to the Federated Traffic Prediction for 5G and Beyond Challenge of the Euclid team and the corresponding paper entitled "Federated Learning for 5G Base Station Traffic Forecasting" by Vasileios Perifanis, Nikolaos Pavlidis, Remous-Aris Koutsiamanis, Pavlos S. Efraimidis, 2022.

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This code can serve as benchmark for federated time-series forecasting. We focus on raw LTE data and train a global federated model using the measurements of three different base stations on different time intervals. Specifically, we implement 6 different model architectures (MLP, RNN, LSTM, GRU, CNN, Dual-Attention LSTM Autoencoder) and 9 different federated aggregation algorithms (SimpleAvg, MedianAvg, FedAvg, FedProx, FedAvgM, FedNova, FedAdagrad, FedYogi, FedAdam) on a non-iid setting with distribution, quantity and temporal skew.

Installation

We recommend using a conda environment with Python 3.8

1. First install PyTorch

$ conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

2. Install additional dependencies

$ pip install pandas scikit_learn matplotlib seaborn colorcet scipy h5py carbontracker notebook

You can also use the requirements' specification:

$ pip install -r requirements.txt

Project Structure

.
├── dataset                 # .csv files
├──── ...
├── ml                      # Machine learning-specific scipts
├──── fl                    # Federated learning utilities
├────── client              # Client representation
├─────── ...
├────── history             # Keeps track of local and global training history
├─────── ...
├────── server              # Server Implementation
├─────── client_manager.py  # Client manager abstract representation and implementation
├─────── client_proxy.py    # Client abstract representation on the server side
├─────── server.py          # Implements the training logic of federated learning
├─────── aggregation        # Implements the aggregation function
├───────── ...
├─────── defaults.py        # Default methods for client creation and weighted metrics
├─────── client_proxy.py    # PyTorch client proxy implementation
├─────── torch_client.py    # PyTorch client implementation
├──── models                # PyTorch models
├───── ...
├──── utils                 # Utilities which are common in Centralized and FL settings
├────── data_utils.py       # Data pre-processing
├────── helpers.py          # Training helper functions
├────── train_utils.py      # Training pipeline 
├── notebooks               # Tools and utilities
└── README.md

Examples

Refer to notebooks for usage examples.