TY - GEN
T1 - A democratically collaborative learning scheme for fog-enabled pervasive environments
AU - Zhang, Tiehua
AU - Shen, Zhishu
AU - Jin, Jiong
AU - Zheng, Xi
PY - 2020
Y1 - 2020
N2 - The emergence of fog computing has brought unprecedented opportunities to many fields, and it is now feasible to incorporate deep learning at the edge to facilitate the development of pervasive systems (e.g., autonomous driving and smart grids). In this paper, we present our preliminary research on a democratic learning scheme so that fog nodes could collaborate on the model training process even without the support of the cloud, which is urgently needed in the pervasive computing context. The main objective of this work is to utilize the deployed fog nodes to train a well-performed deep learning model together, even with the limited local data from each participant. Instead of relying on the cloud by default, we design a voting strategy so that a fog node could be elected as the coordinator based on both distance and computational power metrics to help expedite the training process. We then experiment the effectiveness of the scheme through a real-world, in-door fog deployment and verify the performance of the trained model through a human moving trajectory tracking use case.
AB - The emergence of fog computing has brought unprecedented opportunities to many fields, and it is now feasible to incorporate deep learning at the edge to facilitate the development of pervasive systems (e.g., autonomous driving and smart grids). In this paper, we present our preliminary research on a democratic learning scheme so that fog nodes could collaborate on the model training process even without the support of the cloud, which is urgently needed in the pervasive computing context. The main objective of this work is to utilize the deployed fog nodes to train a well-performed deep learning model together, even with the limited local data from each participant. Instead of relying on the cloud by default, we design a voting strategy so that a fog node could be elected as the coordinator based on both distance and computational power metrics to help expedite the training process. We then experiment the effectiveness of the scheme through a real-world, in-door fog deployment and verify the performance of the trained model through a human moving trajectory tracking use case.
UR - http://www.scopus.com/inward/record.url?scp=85091973601&partnerID=8YFLogxK
U2 - 10.1109/PerComWorkshops48775.2020.9156153
DO - 10.1109/PerComWorkshops48775.2020.9156153
M3 - Conference proceeding contribution
T3 - International Conference on Pervasive Computing and Communications
BT - 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
PB - Institute of Electrical and Electronics Engineers (IEEE)
CY - Piscataway, NJ
T2 - IEEE International Conference on Pervasive Computing and Communications (2020 : 18th)
Y2 - 23 March 2020 through 27 March 2020
ER -