Abstract
Consumer energy data presents unique challenges in terms of privacy and security due to the vast amounts generated by IoT-based devices. Federated learning (FL) has emerged as a privacy-preserving and cost-efficient approach for training machine learning models, offering solutions to protect consumers' data from breaches. However, FL models are still vulnerable to privacy attacks. To mitigate these risks, differential privacy (DP) is applied to local model weights, enhancing privacy but often reducing prediction accuracy. To address this trade-off, we propose a novel single-layer federated weight aggregation framework for consumer energy load forecasting. This privacy-aware approach aggregates only the single layer of local models, ignoring other layers to minimize privacy risks. Extensive simulations with real-world consumer energy data show the framework's effectiveness, achieving a Mean Absolute Error (MAE) of 1.13kWh, close to the baseline of 1.01kWh. It significantly outperforms traditional FL-based methods (FedAvg), which have an MAE of 4.2kWh at a noise level (ϵ) of 0.2, while providing 211.09 times better communication cost efficiency.
Original language | English |
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Specialist publication | IEEE Consumer Electronics Magazine |
Publisher | Institute of Electrical and Electronics Engineers |
DOIs | |
Publication status | E-pub ahead of print - 18 Nov 2024 |
ASJC Scopus subject areas
- Human-Computer Interaction
- Hardware and Architecture
- Computer Science Applications
- Electrical and Electronic Engineering