38 citations · 77 across the 12 of their papers we have counts for
12 papers
Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learning
Hao Chen, Yu Ye, Ming Xiao +1
Machine learning (ML) is a key technique for big-data-driven modelling and analysis of massive Internet of Things (IoT) based intelligent and ubiquitous computing. For fast-increas…
Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT
Wanlu Lei, Yu Ye, Ming Xiao +2
Edge computing provides a promising paradigm to support the implementation of Industrial Internet of Things (IIoT) by offloading tasks to nearby edge nodes. Meanwhile, the increasi…
Coded Stochastic ADMM for Decentralized Consensus Optimization with Edge Computing
Hao Chen, Yu Ye, Ming Xiao +2
Big data, including applications with high security requirements, are often collected and stored on multiple heterogeneous devices, such as mobile devices, drones and vehicles. Due…
Fully Decentralized Federated Learning Based Beamforming Design for UAV Communications
Yue Xiao, Yu Ye, Shaocheng Huang +4
To handle the data explosion in the era of internet of things (IoT), it is of interest to investigate the decentralized network, with the aim at relaxing the burden to central serv…
Decentralized Beamforming Design for Intelligent Reflecting Surface-enhanced Cell-free Networks
Shaocheng Huang, Yu Ye, Ming Xiao +2
Cell-free networks are considered as a promising distributed network architecture to satisfy the increasing number of users and high rate expectations in beyond-5G systems. However…
Deep Reinforcement Learning Based Spectrum Allocation in Integrated Access and Backhaul Networks
Wanlu Lei, Yu Ye, Ming Xiao
We develop a framework based on deep reinforce-ment learning (DRL) to solve the spectrum allocation problem inthe emerging integrated access and backhaul (IAB) architecturewith lar…