88 citations · 301 across the 21 of their papers we have counts for
5 papers · 1 filter
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…
Blockchain Assisted Federated Learning over Wireless Channels: Dynamic Resource Allocation and Client Scheduling
Xiumei Deng, Jun Li, Chuan Ma +5
The blockchain technology has been extensively studied to enable distributed and tamper-proof data processing in federated learning (FL). Most existing blockchain assisted FL (BFL)…
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…