1.4k citations · 1.5k across the 17 of their papers we have counts for
7 papers · 1 filter
Federated Learning for Industrial Internet of Things in Future Industries
Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana +4
The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries. Recently,…
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,…
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…