# eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference

arXiv: https://arxiv.org/abs/2205.05756 · HTML: https://mlsanity.com/paper/2205.05756/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 11 May 2022.

## Builds on (most cited first, 9 of 9)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [A Survey on Distributed Machine Learning](https://mlsanity.com/paper/1912.09789/) (2019)
- [Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach](https://mlsanity.com/paper/2003.08725/) (2020)
- [The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification](https://mlsanity.com/paper/1704.01664/) (2017)
- [Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach](https://mlsanity.com/paper/1905.07479/) (2019)
- [Model Fusion with Kullback--Leibler Divergence](https://mlsanity.com/paper/2007.06168/) (2020)
- [A context-aware pedestrian trajectory prediction framework for automated vehicles](https://mlsanity.com/paper/2104.08123/) (2021)
- [Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel Survey](https://mlsanity.com/paper/1904.08933/) (2019)

## Built on by (most cited first, 1 of 1)

- [Survey of Federated Learning Models for Spatial-Temporal Mobility Applications](https://mlsanity.com/paper/2305.05257/) (2023)

## Abstract (arXiv)

As the most significant data source in smart mobility systems, GPS trajectories can help identify user travel mode. However, these GPS datasets may contain users' private information (e.g., home location), preventing many users from sharing their private information with a third party. Hence, identifying travel modes while protecting users' privacy is a significant issue. To address this challenge, we use federated learning (FL), a privacy-preserving machine learning technique that aims at collaboratively training a robust global model by accessing users' locally trained models but not their raw data. Specifically, we designed a novel ensemble-based Federated Deep Neural Network (eFedDNN). The ensemble method combines the outputs of the different models learned via FL by the users and shows an accuracy that surpasses comparable models reported in the literature. Extensive experimental studies on a real-world open-access dataset from Montreal demonstrate that the proposed inference model can achieve accurate identification of users' mode of travel without compromising privacy.