TY - GEN
T1 - Federated Learning Aided Deep Convolutional Neural Network Solution for Smart Traffic Management
AU - Liu, Guanxiong
AU - Furth, Nicholas
AU - Shi, Hang
AU - Khreishah, Abdallah
AU - Lee, Jo Young
AU - Ansari, Nirwan
AU - Liu, Chengjun
AU - Jararweh, Yaser
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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%.
AB - 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%.
KW - Machine Learning
KW - Neural Network
KW - Traffic Video Analytic
UR - http://www.scopus.com/inward/record.url?scp=85164730352&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85164730352&partnerID=8YFLogxK
U2 - 10.1109/NOMS56928.2023.10154300
DO - 10.1109/NOMS56928.2023.10154300
M3 - Conference contribution
AN - SCOPUS:85164730352
T3 - Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023
BT - Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023
A2 - Akkaya, Kemal
A2 - Festor, Olivier
A2 - Fung, Carol
A2 - Rahman, Mohammad Ashiqur
A2 - Granville, Lisandro Zambenedetti
A2 - dos Santos, Carlos Raniery Paula
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023
Y2 - 8 May 2023 through 12 May 2023
ER -