88 citations · 301 across the 21 of their papers we have counts for
5 papers · 1 filter
Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients
Jun Li, Yumeng Shao, Ming Ding +4
Federated learning (FL), as a distributed machine learning approach, has drawn a great amount of attention in recent years. FL shows an inherent advantage in privacy preservation,…
When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm
Chuan Ma, Jun Li, Ming Ding +4
Motivated by the explosive computing capabilities at end user equipments, as well as the growing privacy concerns over sharing sensitive raw data, a new machine learning paradigm,…
RDP-GAN: A Rényi-Differential Privacy based Generative Adversarial Network
Chuan Ma, Jun Li, Ming Ding +4
Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection. Without…
Dynamic Virtual Resource Allocation for 5G and Beyond Network Slicing
Fei Song, Jun Li, Chuan Ma +3
The fifth generation and beyond wireless communication will support vastly heterogeneous services and use demands such as massive connection, low latency and high transmission rate…
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization
Kang Wei, Jun Li, Ming Ding +4
Federated learning (FL), as a type of collaborative machine learning framework, is capable of preserving private data from mobile terminals (MTs) while training the data into usefu…