38 citations · 50 across the 7 of their papers we have counts for
7 papers
Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs
Francesc Wilhelmi, Jernej Hribar, Selim F. Yilmaz +10
As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency. To unlea…
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…
Satellite Based Computing Networks with Federated Learning
Hao Chen, Ming Xiao, Zhibo Pang
Driven by the ever-increasing penetration and proliferation of data-driven applications, a new generation of wireless communication, the sixth-generation (6G) mobile system enhance…
Federated Learning over Wireless IoT Networks with Optimized Communication and Resources
Hao Chen, Shaocheng Huang, Deyou Zhang +3
To leverage massive distributed data and computation resources, machine learning in the network edge is considered to be a promising technique especially for large-scale model trai…
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…
Privacy-preserving Incremental ADMM for Decentralized Consensus Optimization
Yu Ye, Hao Chen, Ming Xiao +2
The alternating direction method of multipliers (ADMM) has been recently recognized as a promising optimizer for large-scale machine learning models. However, there are very few re…