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Updated: Jun 24, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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PODE: privacy-enhanced distributed federated learning approach for origin-destination estimation.

Sidra Abbas1, Gabriel Avelino Sampedro2,3, Ahmad Almadhor4

  • 1Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan.

Peerj. Computer Science
|June 10, 2024
PubMed
Summary

This study introduces PODE, a federated learning (FL) approach for truck destination prediction. PODE trains deep neural networks locally, preserving privacy and achieving 93.20% accuracy without sharing raw data.

Keywords:
Distributed learningFederated learningFreight generationRegional freight demand model

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Area of Science:

  • Transportation modeling
  • Machine learning in logistics
  • Data privacy in AI

Background:

  • Consumer transportation demand models analyze travel behavior to predict future needs.
  • Federated learning (FL) enables decentralized model training without raw data exchange.
  • Previous research utilized naturalistic driving, crash data, and simulations to understand vehicle design's impact on safety.

Purpose of the Study:

  • To propose PODE, a novel approach using federated learning (FL) to train a deep neural network (DNN) for predicting truck destinations.
  • To preserve sensitive individual location information during origin-destination (OD) estimation by training models locally on decentralized devices.
  • To develop an efficient and privacy-preserving method for truck routing and logistics optimization.

Main Methods:

  • Utilized a customized deep neural network (DNN) architecture based on federated learning (FL) with a two-client, one-server setup.
  • Implemented key preprocessing procedures, including reducing the number of target labels from 51 to 11 for enhanced learning efficiency.
  • Trained local models on client devices, with model updates aggregated by the server to form a global model, facilitating distributed training.

Main Results:

  • The proposed PODE methodology achieved a high accuracy of 93.20% on the server side.
  • FL architecture successfully trained the DNN across decentralized devices without compromising raw data privacy.
  • The two-client, one-server architecture effectively reduced the server's computational load and enabled distributed training.

Conclusions:

  • PODE offers an effective and privacy-preserving solution for truck destination prediction using federated learning.
  • The approach demonstrates the viability of distributed deep learning for origin-destination estimation in transportation.
  • Achieving 93.20% accuracy highlights the potential of FL in enhancing logistics and transportation safety.