Privacy-Enhancing Federated Time-Series Forecasting: A Microaggregation-Based Approach

Sargam Gupta, Vicenç Torra

2025

Abstract

Time-series forecasting is predicting future values based on historical data. Applications include forecasting traffic flows, stock market trends, and energy consumption, which significantly helps to reduce costs and efficiency. However, the complexity inherent in time-series data makes accurate forecasting challenging. This article proposes a novel privacy-enhancing k-anonymous federated learning framework for time-series prediction based on microaggregation. This adaptable framework can be customised based on the client-side processing capabilities. We evaluate the performance of our proposed framework by comparing it with the centralized one using the standard metrics like Mean Absolute Error on three real-world datasets. Moreover, we performed a detailed ablation study by experimenting with different values of k in microaggregation and different client side forecasting models. The results show that our approach gives comparable a good privacy-utility tradeoff as compared to the centralized benchmark.

Paper Citation

in Harvard Style

Gupta S. and Torra V. (2025). Privacy-Enhancing Federated Time-Series Forecasting: A Microaggregation-Based Approach. In Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-760-3, SciTePress, pages 765-770. DOI: 10.5220/0013641100003979

in Bibtex Style

@conference{secrypt25,
author={Sargam Gupta and Vicenç Torra},
title={Privacy-Enhancing Federated Time-Series Forecasting: A Microaggregation-Based Approach},
booktitle={Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2025},
pages={765-770},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013641100003979},
isbn={978-989-758-760-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - Privacy-Enhancing Federated Time-Series Forecasting: A Microaggregation-Based Approach
SN - 978-989-758-760-3
AU - Gupta S.
AU - Torra V.
PY - 2025
SP - 765
EP - 770
DO - 10.5220/0013641100003979
PB - SciTePress