Federated Learning Aided Deep Convolutional Neural Network Solution for Smart Traffic Management

Guanxiong Liu, Nicholas Furth, Hang Shi, Abdallah Khreishah, Jo Young Lee, Nirwan Ansari, Chengjun Liu, Yaser Jararweh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Machine learning models, especially neural network (NN) classifiers, have shown tremendous potential of being used in complex tasks such as image classification, object detection and video analytics. However, to be adopted in the real-world applications, there are still problems to be answered. One of these problems is that training machine learning models, especially NN models, requires a certain level of computation and data processing. Other problems are the limited bandwidth of the network and the possibility of exposing the privacy of the users to attacks if the training data (specially video) is going to be transferred through the network. To mitigate these problems, researchers recently proposed the concept of federated learning.In this paper, we build a video analytic application for traffic management and train it using federated learning. More specifically, each traffic surveillance camera combined with its co-located small PC are seen as the worker node in federated learning. In this way, the NN model in each node can be trained on data collected from all nodes without transmitting and sharing with a central server, which resolves all of the above mentioned problems. The performance of the trained NN model is evaluated via experiments under different open sourced datasets to demonstrate that the proposed work has the potential to enhance the detection accuracy (mAP) over 40%.

Original languageEnglish (US)
Title of host publicationProceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023
EditorsKemal Akkaya, Olivier Festor, Carol Fung, Mohammad Ashiqur Rahman, Lisandro Zambenedetti Granville, Carlos Raniery Paula dos Santos
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665477161
DOIs
StatePublished - 2023
Event36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023 - Miami, United States
Duration: May 8 2023May 12 2023

Publication series

NameProceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023

Conference

Conference36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023
Country/TerritoryUnited States
CityMiami
Period5/8/235/12/23

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Modeling and Simulation

Keywords

  • Machine Learning
  • Neural Network
  • Traffic Video Analytic

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