Summary of Privacy-preserved Taxi Demand Prediction System Utilizing Distributed Data, by Ren Ozeki et al.
Privacy-Preserved Taxi Demand Prediction System Utilizing Distributed Data
by Ren Ozeki, Haruki Yonekura, Hamada Rizk, Hirozumi Yamaguchi
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed CC-Net model uses collaborative learning enhanced with contrastive learning for accurate taxi-demand prediction, addressing concerns around customer data privacy and security. This novel approach enables multiple parties to collaboratively train a demand-prediction model through hierarchical federated learning, clustering similar parties together without exchanging data. The results demonstrate improved prediction accuracy by at least 2.2% compared to existing techniques, while maintaining the privacy of customers’ data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CC-Net is a new way to predict how many taxis are needed in a city. It makes sure that people’s personal information stays private and safe. This is important because cities need accurate predictions to make their taxi services better. The old way of doing this was called federated learning, but it had some problems. CC-Net fixes these issues by grouping similar places together and training the model within each group without sharing data. This makes sure that people’s information stays private. |
Keywords
» Artificial intelligence » Clustering » Federated learning
