88 citations · 107 across the 4 of their papers we have counts for
7 papers
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization
Kang Wei, Jun Li, Ming Ding +3
Federated learning (FL), as a type of distributed machine learning frameworks, is vulnerable to external attacks on FL models during parameters transmissions. An attacker in FL may…
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,…
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…
Federated Learning with Differential Privacy: Algorithms and Performance Analysis
Kang Wei, Jun Li, Ming Ding +6
In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noises are added to t…