295 citations · 405 across the 47 of their papers we have counts for
14 papers · 1 filter
Advancing RAN Slicing with Offline Reinforcement Learning
Kun Yang, Shu-ping Yeh, Menglei Zhang +3
Dynamic radio resource management (RRM) in wireless networks presents significant challenges, particularly in the context of Radio Access Network (RAN) slicing. This technology, cr…
Harnessing the Power of Federated Learning in Federated Contextual Bandits
Chengshuai Shi, Ruida Zhou, Kun Yang +1
Federated learning (FL) has demonstrated great potential in revolutionizing distributed machine learning, and tremendous efforts have been made to extend it beyond the original foc…
Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets
Kun Yang, Cong Shen, Jing Yang +2
The recent development of reinforcement learning (RL) has boosted the adoption of online RL for wireless radio resource management (RRM). However, online RL algorithms require dire…
Federated Linear Bandits with Finite Adversarial Actions
Li Fan, Ruida Zhou, Chao Tian +1
We study a federated linear bandits model, where clients communicate with a central server to solve a linear contextual bandits problem with finite adversarial action sets that…
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization
Wei Shen, Minhui Huang, Jiawei Zhang +1
In recent years, federated minimax optimization has attracted growing interest due to its extensive applications in various machine learning tasks. While Smoothed Alternative Gradi…
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources
Chengshuai Shi, Wei Xiong, Cong Shen +1
Existing theoretical studies on offline reinforcement learning (RL) mostly consider a dataset sampled directly from the target task. In practice, however, data often come from seve…
Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints
Donghao Li, Ruiquan Huang, Cong Shen +1
This paper investigates conservative exploration in reinforcement learning where the performance of the learning agent is guaranteed to be above a certain threshold throughout the…
Differentially Private Wireless Federated Learning Using Orthogonal Sequences
Xizixiang Wei, Tianhao Wang, Ruiquan Huang +3
We propose a privacy-preserving uplink over-the-air computation (AirComp) method, termed FLORAS, for single-input single-output (SISO) wireless federated learning (FL) systems. Fro…
On High-dimensional and Low-rank Tensor Bandits
Chengshuai Shi, Cong Shen, Nicholas D. Sidiropoulos
Most existing studies on linear bandits focus on the one-dimensional characterization of the overall system. While being representative, this formulation may fail to model applicat…
Reward Teaching for Federated Multi-armed Bandits
Chengshuai Shi, Wei Xiong, Cong Shen +1
Most of the existing federated multi-armed bandits (FMAB) designs are based on the presumption that clients will implement the specified design to collaborate with the server. In r…
Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning
Jieming Bian, Cong Shen, Jie Xu
Federated Learning (FL) is a machine learning approach that enables the creation of shared models for powerful applications while allowing data to remain on devices. This approach…
Accelerating Hybrid Federated Learning Convergence under Partial Participation
Jieming Bian, Lei Wang, Kun Yang +2
Over the past few years, Federated Learning (FL) has become a popular distributed machine learning paradigm. FL involves a group of clients with decentralized data who collaborate…
Federated Learning via Indirect Server-Client Communications
Jieming Bian, Cong Shen, Jie Xu
Federated Learning (FL) is a communication-efficient and privacy-preserving distributed machine learning framework that has gained a significant amount of research attention recent…
Communication and Storage Efficient Federated Split Learning
Yujia Mu, Cong Shen
Federated learning (FL) is a popular distributed machine learning (ML) paradigm, but is often limited by significant communication costs and edge device computation capabilities. F…