237 citations · 336 across the 11 of their papers we have counts for
13 papers
Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training
Shenglai Zeng, Zonghang Li, Hongfang Yu +4
Federated learning (FL) is a rapidly growing privacy-preserving collaborative machine learning paradigm. In practical FL applications, local data from each data silo reflect local…
Securing Federated Learning: A Covert Communication-based Approach
Yuan-Ai Xie, Jiawen Kang, Dusit Niyato +4
Federated Learning Networks (FLNs) have been envisaged as a promising paradigm to collaboratively train models among mobile devices without exposing their local privacy data. Due t…
GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning
Zelei Liu, Yuanyuan Chen, Han Yu +2
Federated Learning (FL) bridges the gap between collaborative machine learning and preserving data privacy. To sustain the long-term operation of an FL ecosystem, it is important t…
Personalised Federated Learning: A Combinational Approach
Sone Kyaw Pye, Han Yu
Federated learning (FL) is a distributed machine learning approach involving multiple clients collaboratively training a shared model. Such a system has the advantage of more train…
Towards Cost-Optimal Policies for DAGs to Utilize IaaS Clouds with Online Learning
Xiaohu Wu, Han Yu, Giuliano Casale +1
Premier cloud service providers (CSPs) offer two types of purchase options, namely on-demand and spot instances, with time-varying features in availability and price. Users like st…
SCNet: A Neural Network for Automated Side-Channel Attack
Guanlin Li, Chang Liu, Han Yu +4
The side-channel attack is an attack method based on the information gained about implementations of computer systems, rather than weaknesses in algorithms. Information about syste…