66 citations · 72 across the 9 of their papers we have counts for
9 papers
Can We Trust the Similarity Measurement in Federated Learning?
Zhilin Wang, Qin Hu, Xukai Zou
Is it secure to measure the reliability of local models by similarity in federated learning (FL)? This paper delves into an unexplored security threat concerning applying similarit…
PoFEL: Energy-efficient Consensus for Blockchain-based Hierarchical Federated Learning
Shengyang Li, Qin Hu, Zhilin Wang
Facilitated by mobile edge computing, client-edge-cloud hierarchical federated learning (HFL) enables communication-efficient model training in a widespread area but also incurs ad…
Straggler Mitigation and Latency Optimization in Blockchain-based Hierarchical Federated Learning
Zhilin Wang, Qin Hu, Minghui Xu +1
Cloud-edge-device hierarchical federated learning (HFL) has been recently proposed to achieve communication-efficient and privacy-preserving distributed learning. However, there ex…
Blockchain-based Edge Resource Sharing for Metaverse
Zhilin Wang, Qin Hu, Minghui Xu +1
Although Metaverse has recently been widely studied, its practical application still faces many challenges. One of the severe challenges is the lack of sufficient resources for com…
Resource Optimization for Blockchain-based Federated Learning in Mobile Edge Computing
Zhilin Wang, Qin Hu, Zehui Xiong
With the development of mobile edge computing (MEC) and blockchain-based federated learning (BCFL), a number of studies suggest deploying BCFL on edge servers. In this case, resour…
Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning
Zhilin Wang, Qin Hu, Ruinian Li +2
Blockchain-based federated learning (BCFL) has recently gained tremendous attention because of its advantages such as decentralization and privacy protection of raw data. However,…
Defense Strategies Toward Model Poisoning Attacks in Federated Learning: A Survey
Zhilin Wang, Qiao Kang, Xinyi Zhang +1
Advances in distributed machine learning can empower future communications and networking. The emergence of federated learning (FL) has provided an efficient framework for distribu…
Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing
Qin Hu, Zhilin Wang, Minghui Xu +1
Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the co…
A Systematic Survey of Blockchained Federated Learning
Zhilin Wang, Qin Hu, Minghui Xu +3
With the technological advances in machine learning, effective ways are available to process the huge amount of data generated in real life. However, issues of privacy and scalabil…