Interpretable Privacy-Preserving Collaborative Deep Learning for Taxi Trip Duration Forecasting

Jelena Fiosina

Abstract: Traffic data are obtained from various distributed sources such as infrastructure and vehicle sensors developed by various organisations, and often cannot be processed together because of data privacy regulations. Thus, distributed machine learning methods are required to process the data without sharing them. Federated learning allows the processing of data distributed by transmitting only the parameters without sharing the real data. The federated learning architecture is based mainly on deep learning, which is often more accurate than other machine learning approaches. However, deep-learning-based models are black-box models, and should be explained to increase trust in the system for both users and developers. Despite the fact that various explainability methods have been proposed, the solutions for explainable federated models are insufficient.

External IDs:dblp:conf/smartgreens/Fiosina21