Jiechao Gao
ABSTRACT
The fast-growing trend of Internet of Things (IoT) has provided its users with opportunities to improve user experience such as voice assistants, smart cameras, and home energy management systems. Such smart home applications often require large numbers of diverse training data to accomplish a robust model. As single user may not have enough data to train such a model, users intent to collaboratively train their collected data in order to achieve better performance in such applications, which raise the concern of data privacy protection. Existing approaches for collaborative training need to aggregate data or intermediate model training updates in the cloud to perform load forecasting, which could directly or indirectly cause personal data leakage, alongside with significant communication bandwidth and extra cloud service monetary cost.
In this paper, to ensure the performance of smart home applications as well as the protection of user data privacy, we introduce the decentralized federated learning framework for the neighborhood and show the study on residential building load forecasting application as an example. We present PriResi, a privacy-preserved, communication-efficient and cloud-service-free load forecasting system to solve the above problems in a residential building. We first introduce a decentralized federated learning framework, which allows the residents to process all collected data locally on the edge by broadcasting the model updates between the smart home agent in each residence. Second, we propose a gradient selection mechanism to reduce the number of aggregated gradients and the frequency of gradient broadcasting to achieve communication-efficient and high prediction results. The real-word dataset experiments show that our method can achieve 97% of load forecasting accuracy while preserving residences' privacy. We believe that our proposed decentralized federated learning framework can be widely used in other smart home applications as well.
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Index Terms
- Decentralized Federated Learning Framework for the Neighborhood: A Case Study on Residential Building Load Forecasting
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