Követés
Shiqiang Wang
Shiqiang Wang
IBM T. J. Watson Research Center
E-mail megerősítve itt: us.ibm.com - Kezdőlap
Cím
Hivatkozott rá
Hivatkozott rá
Év
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
20892019
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
5892021
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
5782018
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
5212023
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
3532015
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
3272017
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
3222017
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
3212021
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
2982015
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
2512019
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
2392018
Online Placement of Multi-Component Applications in Edge Computing Environments
S Wang, M Zafer, KK Leung
IEEE Access, 2017
2292017
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
2122021
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
2042020
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
1952022
Service placement with provable guarantees in heterogeneous edge computing systems
S Pasteris, S Wang, M Herbster, T He
IEEE INFOCOM 2019, 2019
1732019
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
168*2020
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
1582014
Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD
J Wang, S Wang, RR Chen, M Ji
AAAI Conference on Artificial Intelligence, 2022
114*2022
Cost-Effective Federated Learning in Mobile Edge Networks
B Luo, X Li, S Wang, J Huang, L Tassiulas
IEEE Journal on Selected Areas in Communications 39 (12), 3606-3621, 2021
882021
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