1.2k citations · 1.8k across the 28 of their papers we have counts for
29 papers
IP Protection in the Era of Visual Generative AI: A Survey
Zhuan Shi, Shunchang Liu, Alireza Dehghanpour Farashah +8
The rapid evolution of visual generative AI has introduced a wide range of intellectual property risks, spanning the unauthorized learning, reproduction, extraction, misuse, and re…
The Prospect of Enhancing Large-Scale Heterogeneous Federated Learning with Transformers
Yulan Gao, Zhaoxiang Hou, Chengyi Yang +2
Federated learning (FL) addresses data privacy concerns by enabling collaborative training of AI models across distributed data owners. Wide adoption of FL faces the fundamental ch…
Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning
Rui Liu, Yuanyuan Chen, Anran Li +3
Insufficient data is a long-standing challenge for Brain-Computer Interface (BCI) to build a high-performance deep learning model. Though numerous research groups and institutes co…
Fairness-Aware Client Selection for Federated Learning
Yuxin Shi, Zelei Liu, Zhuan Shi +1
Federated learning (FL) has enabled multiple data owners (a.k.a. FL clients) to train machine learning models collaboratively without revealing private data. Since the FL server ca…
Multi-Tier Client Selection for Mobile Federated Learning Networks
Yulan Gao, Yansong Zhao, Han Yu
Federated learning (FL), which addresses data privacy issues by training models on resource-constrained mobile devices in a distributed manner, has attracted significant research a…
FedGH: Heterogeneous Federated Learning with Generalized Global Header
Liping Yi, Gang Wang, Xiaoguang Liu +2
Federated learning (FL) is an emerging machine learning paradigm that allows multiple parties to train a shared model collaboratively in a privacy-preserving manner. Existing horiz…
Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout
Yuanyuan Chen, Zichen Chen, Sheng Guo +6
Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications. Since complex industrial systems often i…
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning
Anran Li, Hongyi Peng, Lan Zhang +4
Vertical Federated Learning (VFL) enables multiple data owners, each holding a different subset of features about largely overlapping sets of data sample(s), to jointly train a use…
Towards Interpretable Federated Learning
Anran Li, Rui Liu, Ming Hu +4
Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data. In order for FL to achieve widespre…
Adaptive Resource Allocation in Quantum Key Distribution (QKD) for Federated Learning
Rakpong Kaewpuang, Minrui Xu, Dusit Niyato +3
Increasing privacy and security concerns in intelligence-native 6G networks require quantum key distribution-secured federated learning (QKD-FL), in which data owners connected via…
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning
Yuanyuan Chen, Zichen Chen, Pengcheng Wu +1
Large-scale neural networks possess considerable expressive power. They are well-suited for complex learning tasks in industrial applications. However, large-scale models pose sign…
Federated Graph Neural Networks: Overview, Techniques and Challenges
Rui Liu, Pengwei Xing, Zichao Deng +3
With its capability to deal with graph data, which is widely found in practical applications, graph neural networks (GNNs) have attracted significant research attention in recent y…
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…
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…
Privacy and Robustness in Federated Learning: Attacks and Defenses
Lingjuan Lyu, Han Yu, Xingjun Ma +5
As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (…
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…
Optimal Procurement Auction for Cooperative Production of Virtual Products: Vickrey-Clarke-Groves Meet Cremer-McLean
Mingshu Cong, Xi Weng, Han Yu +2
We set up a supply-side game-theoretic model for the cooperative production of virtual products. In our model, a group of producers collaboratively produce a virtual product by con…
Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention
Ce Ju, Ruihui Zhao, Jichao Sun +11
Prevention of stroke with its associated risk factors has been one of the public health priorities worldwide. Emerging artificial intelligence technology is being increasingly adop…