Shiqiang Wang
Shiqiang Wang
IBM T. J. Watson Research Center
Dirección de correo verificada de - Página principal
Citado por
Citado por
Adaptive federated learning in resource constrained edge computing systems
S Wang, T Tuor, T Salonidis, KK Leung, C Makaya, T He, K Chan
IEEE Journal on Selected Areas in Communications 37 (6), 1205-1221, 2019
When edge meets learning: Adaptive control for resource-constrained distributed machine learning
S Wang, T Tuor, T Salonidis, KK Leung, C Makaya, T He, K Chan
IEEE INFOCOM 2018-IEEE Conference on Computer Communications, 63-71, 2018
A survey on federated learning for resource-constrained IoT devices
A Imteaj, U Thakker, S Wang, J Li, MH Amini
IEEE Internet of Things Journal 9 (1), 1-24, 2021
Model pruning enables efficient federated learning on edge devices
Y Jiang, S Wang, V Valls, BJ Ko, WH Lee, KK Leung, L Tassiulas
IEEE Transactions on Neural Networks and Learning Systems, 2023
Dynamic service migration in mobile edge-clouds
S Wang, R Urgaonkar, M Zafer, T He, K Chan, KK Leung
2015 IFIP Networking Conference (IFIP Networking), 1-9, 2015
Live service migration in mobile edge clouds
A Machen, S Wang, KK Leung, BJ Ko, T Salonidis
IEEE Wireless Communications 25 (1), 140-147, 2017
Dynamic service placement for mobile micro-clouds with predicted future costs
S Wang, R Urgaonkar, K Chan, T He, M Zafer, KK Leung
IEEE Transactions on Parallel and Distributed Systems 28 (4), 1002-1016, 2017
Dynamic service migration and workload scheduling in edge-clouds
R Urgaonkar, S Wang, T He, M Zafer, K Chan, KK Leung
Performance Evaluation 91, 205-228, 2015
Service Placement and Request Scheduling for Data-Intensive Applications in Edge Clouds
V Farhadi, F Mehmeti, T He, TF La Porta, H Khamfroush, S Wang, ...
IEEE/ACM Transactions on Networking, 2021
Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process
S Wang, R Urgaonkar, M Zafer, T He, K Chan, KK Leung
IEEE/ACM Transactions on Networking, 2019
It’s Hard to Share: Joint Service Placement and Request Scheduling in Edge Clouds with Sharable and Non-sharable Resources
T He, H Khamfroush, S Wang, TL Porta, S Stein
IEEE ICDCS 2018, 2018
Online Placement of Multi-Component Applications in Edge Computing Environments
S Wang, M Zafer, KK Leung
IEEE Access, 2017
Adaptive gradient sparsification for efficient federated learning: An online learning approach
P Han, S Wang, KK Leung
2020 IEEE 40th International Conference on Distributed Computing Systems …, 2020
Service placement with provable guarantees in heterogeneous edge computing systems
S Pasteris, S Wang, M Herbster, T He
IEEE INFOCOM 2019, 2019
Cost-effective federated learning design
B Luo, X Li, S Wang, J Huang, L Tassiulas
IEEE INFOCOM 2021-IEEE Conference on Computer Communications, 1-10, 2021
Mobility-induced service migration in mobile micro-clouds
S Wang, R Urgaonkar, T He, M Zafer, K Chan, KK Leung
2014 IEEE Military Communications Conference, 835-840, 2014
Overcoming Noisy and Irrelevant Data in Federated Learning
T Tuor, S Wang, BJ Ko, C Liu, KK Leung
International Conference on Pattern Recognition (ICPR), 2020
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling
B Luo, W Xiao, S Wang, J Huang, L Tassiulas
IEEE International Conference on Computer Communications (INFOCOM), 2022
Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD
J Wang, S Wang, RR Chen, M Ji
AAAI Conference on Artificial Intelligence, 2022
Asymptotically optimal algorithm for online reconfiguration of edge-clouds
I Hou, T Zhao, S Wang, K Chan
Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc …, 2016
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20