STV

This repository contains the code for a forecasting framework with vertical federated learning. The methods cover a linear autoregressive forecaster, SARIMAX, and an autoregressive tree-based forecaster: https://github.com/JoaquinAmatRodrigo/skforecast

Relevant Files

1. Secret-shared matrix operations and communication experiments: ssmatrix.py
2. Forecasting tests: forecast_<DATASET>.py
3. Secret-sharing primitives: SSCalculation.py and SSCalculate_Alternate.py
4. VFL XGBoost code based on MP-FedXGB: VerticalXGBoost.py, with documentation: https://github.com/HikariX/MP-FedXGB
5. Original diffusion model for time series repository: https://github.com/AI4HealthUOL/SSSD. Additional results and minor extensions in: https://anonymous.4open.science/r/SSSD-DE14/README.md

Datasets

1. Rossman Sales: https://www.kaggle.com/c/rossmann-store-sales/data
2. Air Quality: https://archive.ics.uci.edu/dataset/360/air+quality
3. Flight Passengers: https://www.kaggle.com/datasets/chirag19/air-passengers
4. SML 2010: https://archive.ics.uci.edu/dataset/274/sml2010
5. PV Power: https://www.kaggle.com/datasets/anikannal/solar-power-generation-data