TY - GEN
T1 - FedCo
T2 - 30th International Conference on Computer Communications and Networks, ICCCN 2021
AU - Balasubramanian, Venkatraman
AU - Aloqaily, Moayad
AU - Reisslein, Martin
N1 - Funding Information:
Supported in part by the U.S. National Science Foundation grant number 1716121
Publisher Copyright:
© 2021 IEEE.
PY - 2021/7
Y1 - 2021/7
N2 - Managing cache content at the edge is one of the many use cases of 5G-And-beyond networks. However, increasing the density of Edge Data Centers (EDCs) to service requests is a crucial problem. To overcome this problem, recent research has advanced mobile device architecture paradigms and the content caching in a Mobile Device Cloud (MDC). These two service locations (EDCs and MDC) are registered with the Mobile Network Operator (MNO), enabling the MNO to control the content placement for profit maximization. As the user demands for content items are directly related to the QoS perceived by the user, it is important to understand the future popularity of the content items and to place them appropriately. Additionally, privacy issues have increased over time because of sensitive user information being divulged at the MDC. To preserve privacy, a branch of machine learning called Federated Learning (FL) can train machine learning models leaving the data in the end user devices. The paper contributions are as follows: (1) We introduce an FL algorithm called FedCo, that trains a deep-neural network (DNN) to predict the user demand of a specific content, so as to manage the content files placement at EDC and MDC sites. (2) We then conduct a theoretical evaluation of user demand behavior via prospect theory to justify revenue maximization for an MNO. (3) We show numerically via a multimedia content delivery use-case how the proposed model compares favorably with two state-of-The-Art designs.
AB - Managing cache content at the edge is one of the many use cases of 5G-And-beyond networks. However, increasing the density of Edge Data Centers (EDCs) to service requests is a crucial problem. To overcome this problem, recent research has advanced mobile device architecture paradigms and the content caching in a Mobile Device Cloud (MDC). These two service locations (EDCs and MDC) are registered with the Mobile Network Operator (MNO), enabling the MNO to control the content placement for profit maximization. As the user demands for content items are directly related to the QoS perceived by the user, it is important to understand the future popularity of the content items and to place them appropriately. Additionally, privacy issues have increased over time because of sensitive user information being divulged at the MDC. To preserve privacy, a branch of machine learning called Federated Learning (FL) can train machine learning models leaving the data in the end user devices. The paper contributions are as follows: (1) We introduce an FL algorithm called FedCo, that trains a deep-neural network (DNN) to predict the user demand of a specific content, so as to manage the content files placement at EDC and MDC sites. (2) We then conduct a theoretical evaluation of user demand behavior via prospect theory to justify revenue maximization for an MNO. (3) We show numerically via a multimedia content delivery use-case how the proposed model compares favorably with two state-of-The-Art designs.
KW - Edge Management
KW - Federated Learning (FL)
KW - Mobile Device Cloud (MDC)
KW - Mobile Edge Computing (MEC)
UR - http://www.scopus.com/inward/record.url?scp=85114963539&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85114963539&partnerID=8YFLogxK
U2 - 10.1109/ICCCN52240.2021.9522153
DO - 10.1109/ICCCN52240.2021.9522153
M3 - Conference contribution
AN - SCOPUS:85114963539
T3 - Proceedings - International Conference on Computer Communications and Networks, ICCCN
BT - 30th International Conference on Computer Communications and Networks, ICCCN 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 July 2021 through 22 July 2021
ER -