A Secure Federated Learning Framework for Residential Short Term Load Forecasting

TL;DR

This paper introduces a differentially private federated learning framework for residential short-term load forecasting that enhances robustness against Byzantine attacks by using gradient sign quantization, ensuring privacy and security.

Contribution

It proposes a novel secure federated learning framework utilizing SignSGD for privacy preservation and Byzantine attack mitigation in load forecasting.

Findings

The framework effectively defends against Byzantine attacks.

It outperforms traditional Fed-SGD models in robustness.

The approach maintains data privacy while ensuring model security.

Abstract

Smart meter measurements, though critical for accurate demand forecasting, face several drawbacks including consumers' privacy, data breach issues, to name a few. Recent literature has explored Federated Learning (FL) as a promising privacy-preserving machine learning alternative which enables collaborative learning of a model without exposing private raw data for short term load forecasting. Despite its virtue, standard FL is still vulnerable to an intractable cyber threat known as Byzantine attack carried out by faulty and/or malicious clients. Therefore, to improve the robustness of federated short-term load forecasting against Byzantine threats, we develop a state-of-the-art differentially private secured FL-based framework that ensures the privacy of the individual smart meter's data while protect the security of FL models and architecture. Our proposed framework leverages the idea of gradient quantization through the Sign Stochastic Gradient Descent (SignSGD) algorithm, where the clients only transmit the `sign' of the gradient to the control centre after local model training. As we highlight through our experiments involving benchmark neural networks with a set of Byzantine attack models, our proposed approach mitigates such threats quite effectively and thus outperforms conventional Fed-SGD models.