Federated Parking Flow Prediction Method Based on Blockchain and IPFS

• Published in: Intelligent Technologies for Interactive Entertainment Vol. 429; pp. 257 - 275
• Main Authors: Zong, Xuesen, Hu, Zhiqiang, Xiong, Xiaoyun, Li, Peng, Wang, Jinlong
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Series: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
• Subjects: Blockchain; Federated learning; Incentive mechanism; IPFS; LSTM; Parking flow prediction
• ISBN: 9783030991876; 3030991873
• ISSN: 1867-8211; 1867-822X
• DOI: 10.1007/978-3-030-99188-3_16

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.