1.2k citations · 1.8k across the 28 of their papers we have counts for
7 papers · 1 filter
Towards Fairness-Aware Federated Learning
Yuxin Shi, Han Yu, Cyril Leung
Recent advances in Federated Learning (FL) have brought large-scale collaborative machine learning opportunities for massively distributed clients with performance and data privacy…
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…
MarS-FL: Enabling Competitors to Collaborate in Federated Learning
Xiaohu Wu, Han Yu
Federated learning (FL) is rapidly gaining popularity and enables multiple data owners ({\em a.k.a.} FL participants) to collaboratively train machine learning models in a privacy-…
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…
Towards Personalized Federated Learning
Alysa Ziying Tan, Han Yu, Lizhen Cui +1
In parallel with the rapid adoption of Artificial Intelligence (AI) empowered by advances in AI research, there have been growing awareness and concerns of data privacy. Recent sig…