29 citations · 36 across the 4 of their papers we have counts for
7 papers
FedNC: A Secure and Efficient Federated Learning Method with Network Coding
Yuchen Shi, Zheqi Zhu, Pingyi Fan +2
Federated Learning (FL) is a promising distributed learning mechanism which still faces two major challenges, namely privacy breaches and system efficiency. In this work, we reconc…
FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learning
Zheqi Zhu, Yuchen Shi, Jiajun Luo +4
Federated learning (FL) has prevailed as an efficient and privacy-preserved scheme for distributed learning. In this work, we mainly focus on the optimization of computation and co…
ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Sampling
Zheqi Zhu, Yuchen Shi, Pingyi Fan +2
As a promising learning paradigm integrating computation and communication, federated learning (FL) proceeds the local training and the periodic sharing from distributed clients. D…
Federated Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computing
Zheqi Zhu, Shuo Wan, Pingyi Fan +1
As an emerging technique, mobile edge computing (MEC) introduces a new processing scheme for various distributed communication-computing systems such as industrial Internet of Thin…
An Importance Aware Weighted Coding Theorem Using Message Importance Measure
Zheqi Zhu, Shanyun Liu, Rui She +3
There are numerous scenarios in source coding where not only the code length but the importance of each value should also be taken into account. Different from the traditional codi…
Storage Space Allocation Strategy for Digital Data with Message Importance
Shanyun Liu, Rui She, Zheqi Zhu +1
This paper mainly focuses on the problem of lossy compression storage from the perspective of message importance when the reconstructed data pursues the least distortion within lim…