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

researchr

You are not signed in
Sign in
Sign up

Chenrui Wu, Yifei Zhu, Rongyu Zhang, Yun Chen, Fangxin Wang 0001, Shuguang Cui. FedAB: Truthful Federated Learning With Auction-Based Combinatorial Multi-Armed Bandit. IEEE Internet of Things Journal, 10(17):15159-15170, September 2023. [doi]

The following publications are possibly variants of this publication:

Auction-Based Combinatorial Multi-Armed Bandit Mechanisms with Strategic ArmsGuoju Gao, He Huang, Mingjun Xiao, Jie Wu, Yu-e Sun, Sheng Zhang. infocom 2021: 1-10 [doi]

Client selection for federated learning using combinatorial multi-armed bandit under long-term energy constraintKonglin Zhu, Fuchun Zhang, Lei Jiao 0002, Bowei Xue, Lin Zhang 0013. cn, 250:110512, 2024. [doi]

Truthful and Dual-Direction Combinatorial Multi-Armed Bandit Scheme to Maximize Profit for Mobile Crowd SensingXiangwan Fu, Saiqin Long, Anfeng Liu, Ju Ren 0001, Bin Guo 0001, Zhetao Li. tdsc, 22(2):1098-1117, March - April 2025. [doi]

Truthful User Recruitment for Cooperative Crowdsensing Task: A Combinatorial Multi-Armed Bandit ApproachHengzhi Wang, Yongjian Yang, En Wang, Wenbin Liu, Yuanbo Xu, Jie Wu 0001. tmc, 22(7):4314-4331, July 2023. [doi]

runs on WebDSL