1.5k citations · 2.9k across the 29 of their papers we have counts for
4 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…
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…