A Federated Meta-Learning Method for Explainable, Privacy-Preserving and Customizable Behavior Analysis

Two standard datasets on travel mode choice are used, namely LPMC and Swissmetro (SM). The LPMC dataset consists of single-day travel diary data obtained by the London Travel Demand Survey from 2012 to 2015. The dataset includes 81,096 samples, each of which corresponds to a trip taken by a person in one of the 17,616 households participating in the survey. The dataset contains four travel modes, namely walking, cycling, public transportation (PT), and car. Moreover, the SM dataset comprises passenger survey data gathered in Switzerland. It includes samples from 1291 passengers across nine travel scenarios designed with three kinds of modes, i.e., train, SM, and car. In the materials, we make separate divisions based on the datasets. Among them, the "data" file contains the original data, the processed data, and the data processing code. The configuration details are described in the "conf" folder. The analysis code is included in the "utilities" file. Then, the codes for the centralized, federated learning framework and the federated meta-learning framework are also attached.