88 citations · 114 across the 7 of their papers we have counts for
10 papers
Low-latency Federated Learning with DNN Partition in Distributed Industrial IoT Networks
Xiumei Deng, Jun Li, Chuan Ma +4
Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with distributed intelligence of industrial automation thanks to its capability of distributed machine learnin…
Low-Latency Federated Learning over Wireless Channels with Differential Privacy
Kang Wei, Jun Li, Chuan Ma +5
In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. The performance of uploaded models in such situations ca…
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,…