221 citations · 343 across the 12 of their papers we have counts for
8 papers · 1 filter
Differentially Private Vertical Federated Learning
Thilina Ranbaduge, Ming Ding
A successful machine learning (ML) algorithm often relies on a large amount of high-quality data to train well-performed models. Supervised learning approaches, such as deep learni…
Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design
Chuan Ma, Jun Li, Ming Ding +3
Owing to the low communication costs and privacy-promoting capabilities, Federated Learning (FL) has become a promising tool for training effective machine learning models among di…
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): Performance Analysis and Resource Allocation
Jun Li, Yumeng Shao, Kang Wei +5
Federated learning (FL), as a distributed machine learning paradigm, promotes personal privacy by local data processing at each client. However, relying on a centralized server for…
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…