@Article{cmes.2023.027032,
AUTHOR = {You Lu, Linqian Cui, Yunzhe Wang, Jiacheng Sun, Lanhui Liu},
TITLE = {Residential Energy Consumption Forecasting Based on Federated Reinforcement Learning with Data Privacy Protection},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {1},
PAGES = {717--732},
URL = {http://www.techscience.com/CMES/v137n1/52346},
ISSN = {1526-1506},
ABSTRACT = {Most studies have conducted experiments on predicting energy consumption by integrating data for model training. However, the process of centralizing data can cause problems of data leakage. Meanwhile, many laws and regulations on data security and privacy have been enacted, making it difficult to centralize data, which can lead to a data silo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework. However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg) method is used to directly weight the model parameters on average, which may have an adverse effect on te model. Therefore, we propose the Federated Reinforcement Learning (FedRL) model, which consists of multiple users collaboratively training the model. Each household trains a local model on local data. These local data never leave the local area, and only the encrypted parameters are uploaded to the central server to participate in the secure aggregation of the global model. We improve FedAvg by incorporating a Q-learning algorithm to assign weights to each locally uploaded local model. And the model has improved predictive performance. We validate the performance of the FedRL model by testing it on a real-world dataset and compare the experimental results with other models. The performance of our proposed method in most of the evaluation metrics is improved compared to both the centralized and distributed models.},
DOI = {10.32604/cmes.2023.027032}
}