Results 221 to 230 of about 33,931 (272)

FedAB: Truthful Federated Learning With Auction-Based Combinatorial Multi-Armed Bandit

IEEE Internet of Things Journal, 2023
Federated learning (FL) emerges as a new distributed machine learning (ML) paradigm that enables thousands of mobile devices to collaboratively train ML models using local data without compromising user privacy.
Chenrui Wu, Yifei Zhu, Rongyu Zhang, Yuxiang Chen, Fangxin Wang, Shuguang Cui   +5 more
semanticscholar   +1 more source

MABFuzz: Multi-Armed Bandit Algorithms for Fuzzing Processors

Design, Automation and Test in Europe, 2023
As the complexities of processors keep increasing, the task of effectively verifying their integrity and security becomes ever more daunting. The intricate web of instructions, microarchitectural features, and interdependencies woven into modern ...
Vasudev Gohil, Rahul Kande, Chen Chen, A. Sadeghi, Jeyavijayan Rajendran   +4 more
semanticscholar   +1 more source

Truthful User Recruitment for Cooperative Crowdsensing Task: A Combinatorial Multi-Armed Bandit Approach

IEEE Transactions on Mobile Computing, 2023
Mobile Crowdsensing (MCS) is a promising paradigm that recruits users to cooperatively perform a sensing task. When recruiting users, existing works mainly focus on selecting a group of users with the best objective ability, e.g., the user's probability ...
Hengzhi Wang, Yongjian Yang, E. Wang, Wenbin Liu, Yuanbo Xu, Jie Wu   +5 more
semanticscholar   +1 more source

Learning With Guarantee Via Constrained Multi-Armed Bandit: Theory and Network Applications

IEEE Transactions on Mobile Computing, 2023
There have been studies that consider optimizing network applications in an online learning context using multi-armed bandit models. However, existing frameworks are problematic as they only consider finding the optimal decisions to minimize the regret ...
Kechao Cai, Xutong Liu, Y. Chen, John C.S. Lui   +3 more
semanticscholar   +1 more source

A Multi-Armed Bandit Approach for Test Case Prioritization in Continuous Integration Environments

IEEE Transactions on Software Engineering, 2022
Continuous Integration (CI) environments have been increasingly adopted in the industry to allow frequent integration of software changes, making software evolution faster and cost-effective.
Jackson A. Prado Lima, S. Vergilio
semanticscholar   +1 more source

Multi-Agent Multi-Armed Bandit Learning for Online Management of Edge-Assisted Computing

IEEE Transactions on Communications, 2021
By orchestrating resources of edge and core network, the delays of edge-assisted computing can decrease. Offloading scheduling is challenging though, especially in the presence of many edge devices with randomly varying link and computing conditions ...
Bochun Wu, Tianyi Chen, Wei Ni, Xin Wang
semanticscholar   +1 more source

Adaptive User-Managed Service Placement for Mobile Edge Computing via Contextual Multi-Armed Bandit Learning

IEEE Transactions on Mobile Computing, 2021
Mobile Edge Computing (MEC), envisioned as a cloud extension, pushes cloud resource from the network core to the network edge, thereby meeting the stringent service requirements of many emerging computation-intensive mobile applications.
Ouyang Tao, Xu Chen, Zhi Zhou, Rui Li, Xin Tang   +4 more
semanticscholar   +1 more source

Combinatorial Multi-Armed Bandit Based Unknown Worker Recruitment in Heterogeneous Crowdsensing

IEEE Conference on Computer Communications, 2020
Mobile crowdsensing, through which a requester can coordinate a crowd of workers to complete some sensing tasks, has attracted significant attention recently. In this paper, we focus on the unknown worker recruitment problem in mobile crowdsensing, where
Guoju Gao, Jie Wu, Mingjun Xiao, Guoliang Chen   +3 more
semanticscholar   +1 more source

Crowdsensing Data Trading based on Combinatorial Multi-Armed Bandit and Stackelberg Game

IEEE International Conference on Data Engineering, 2021
Crowdsensing Data Trading (CDT), through which a platform can aggregate some data collected by a group of mobile users with sensing devices (a.k.a., data sellers) and sell the corresponding statistics to data consumers, has been recognized as a promising
Baoyi An, Mingjun Xiao, An Liu, Xike Xie, Xiaofang Zhou   +4 more
semanticscholar   +1 more source

Multi-Armed bandit problem revisited

Journal of Optimization Theory and Applications, 1994
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ishikida, T., Varaiya, P.
openaire   +2 more sources