15 citations · 52 across the 7 of their papers we have counts for
9 papers
On Federated Learning with Energy Harvesting Clients
Cong Shen, Jing Yang, Jie Xu
Catering to the proliferation of Internet of Things devices and distributed machine learning at the edge, we propose an energy harvesting federated learning (EHFL) framework in thi…
Random Orthogonalization for Federated Learning in Massive MIMO Systems
Xizixiang Wei, Cong Shen, Jing Yang +1
We propose a novel uplink communication method, coined random orthogonalization, for federated learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system.…
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization
Chengshuai Shi, Wei Xiong, Cong Shen +1
Despite the significant interests and many progresses in decentralized multi-player multi-armed bandits (MP-MAB) problems in recent years, the regret gap to the natural centralized…
Federated Linear Contextual Bandits
Ruiquan Huang, Weiqiang Wu, Jing Yang +1
This paper presents a novel federated linear contextual bandits model, where individual clients face different -armed stochastic bandits coupled through common global parameters…
Federated Multi-armed Bandits with Personalization
Chengshuai Shi, Cong Shen, Jing Yang
A general framework of personalized federated multi-armed bandits (PF-MAB) is proposed, which is a new bandit paradigm analogous to the federated learning (FL) framework in supervi…
Stochastic Linear Contextual Bandits with Diverse Contexts
Weiqiang Wu, Jing Yang, Cong Shen
In this paper, we investigate the impact of context diversity on stochastic linear contextual bandits. As opposed to the previous view that contexts lead to more difficult bandit l…