Research Article
Federated Parking Flow Prediction Method Based on Blockchain and IPFS
@INPROCEEDINGS{10.1007/978-3-030-99188-3_16, author={Xuesen Zong and Zhiqiang Hu and Xiaoyun Xiong and Peng Li and Jinlong Wang}, title={Federated Parking Flow Prediction Method Based on Blockchain and IPFS}, proceedings={Intelligent Technologies for Interactive Entertainment. 13th EAI International Conference, INTETAIN 2021, Virtual Event, December 3-4, 2021, Proceedings}, proceedings_a={INTETAIN}, year={2022}, month={3}, keywords={LSTM Federated learning Blockchain IPFS Parking flow prediction Incentive mechanism}, doi={10.1007/978-3-030-99188-3_16} }- Xuesen Zong
Zhiqiang Hu
Xiaoyun Xiong
Peng Li
Jinlong Wang
Year: 2022
Federated Parking Flow Prediction Method Based on Blockchain and IPFS
INTETAIN
Springer
DOI: 10.1007/978-3-030-99188-3_16
Abstract
Aiming at the problem of privacy security of parking data and low generalization performance of parking flow prediction model, a federated parking flow prediction method based on blockchain and IPFS is proposed. In this method, blockchain and IPFS are applied to the federated learning frame-work. Under the condition of ensuring the privacy and security of parking data, blockchain is used to replace the central server of federated learning to aggregate multi-party local models. Through blockchain and IPFS, the model data in the training stage of the parking flow prediction model are stored and synchronized quickly, which improves the generalization performance of the model and further improves the training efficiency of the model. In addition, in order to improve the participation enthusiasm of all participants, an incentive mechanism based on data volume contribution and model performance improvement contribution is designed. The experimental results show that the method can improve the generalization performance of the model and improve the training efficiency of the parking flow prediction model, and provide a reasonable reward allocation.
