no code implementations • 1 May 2024 • Liu Yang, Shuowei Cai, Di Chai, Junxue Zhang, Han Tian, Yilun Jin, Kun Guo, Kai Chen, Qiang Yang
To this core, we propose PackVFL, an efficient VFL framework based on packed HE (PackedHE), to accelerate the existing HE-based VFL algorithms.
1 code implementation • 17 Sep 2023 • Kun Guo, Haochen Zhu, Gang Cao
Powerful manipulation techniques have made digital image forgeries be easily created and widespread without leaving visual anomalies.
no code implementations • 23 Mar 2023 • Chaoqun You, Kun Guo, Gang Feng, Peng Yang, Tony Q. S. Quek
With the obtained FL hyperparameters and resource allocation, we design a MAML-based FL algorithm, called Automated Federated Learning (AutoFL), that is able to conduct fast adaptation and convergence.
no code implementations • 19 Mar 2023 • Chaoqun You, Kun Guo, Howard H. Yang, Tony Q. S. Quek
Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks.
1 code implementation • IEEE Transactions on Emerging Topics in Computing 2022 • Jianping Cai, Ximeng Liu, Zhiyong Yu, Kun Guo, Jiayin Li
The experiments show that our proposed algorithm takes only about 400 seconds to handle up to 9. 6 million large-scale samples, while the state-of-the-art algorithms take close to 1000 seconds to handle every 1000 samples, which embodies the advantage of our algorithms in handling large-scale samples. δ -data indistinguishability theory, we provide quantitative theoretical guarantees for the security of our algorithms.
no code implementations • 28 Sep 2022 • Marie Siew, Shikhar Sharma, Zekai Li, Kun Guo, Chao Xu, Tania Lorido-Botran, Tony Q. S. Quek, Carlee Joe-Wong
In edge computing, users' service profiles are migrated due to user mobility.
no code implementations • 27 Sep 2022 • Chaoqun You, Daquan Feng, Kun Guo, Howard H. Yang, Tony Q. S. Quek
Experimental results verify the effectiveness of PerFedS2 in saving training time as well as guaranteeing the convergence of training loss, in contrast to synchronous and asynchronous PFL algorithms.
no code implementations • 28 Jun 2022 • Shuowei Cai, Di Chai, Liu Yang, Junxue Zhang, Yilun Jin, Leye Wang, Kun Guo, Kai Chen
In this paper, we focus on SplitNN, a well-known neural network framework in VFL, and identify a trade-off between data security and model performance in SplitNN.
no code implementations • 27 Jan 2022 • Peng Wei, Kun Guo, Ye Li, Jue Wang, Wei Feng, Shi Jin, Ning Ge, Ying-Chang Liang
Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond.
no code implementations • 18 Aug 2021 • Liu Yang, Junxue Zhang, Di Chai, Leye Wang, Kun Guo, Kai Chen, Qiang Yang
In this paper, we proposed federated masked matrix factorization (FedMMF) to protect the data privacy in federated recommender systems without sacrificing efficiency and effectiveness.
no code implementations • 5 Mar 2021 • Yanli Yuan, De Wen Soh, Xiao Yang, Kun Guo, Tony Q. S. Quek
Theoretically, we provide a theoretical analysis of the proposed graph estimator, which establishes a non-asymptotic bound of the estimation error under the high-dimensional setting and reflects the effect of several key factors on the convergence rate of our algorithm.
no code implementations • 25 Feb 2021 • Peng Yang, Kun Guo, Xing Xi, Tony Q. S. Quek, Xianbin Cao, Chenxi Liu
Particularly, we first propose to decompose the sequential decision problem into multiple repeated optimization subproblems via a Lyapunov technique.
Networking and Internet Architecture Signal Processing
no code implementations • 28 Nov 2020 • Souheil Fenghour, Daqing Chen, Kun Guo, Perry Xiao
This paper proposes a method to tackle the one-to-many mapping problem when performing automated lip reading using solely visual cues in two separate scenarios: the first scenario is where the word boundary, that is, the beginning and the ending of a word, is unknown; and the second scenario is where the boundary is known.
1 code implementation • 5 Jul 2020 • Wenchao Xia, Tony Q. S. Quek, Kun Guo, Wanli Wen, Howard H. Yang, Hongbo Zhu
In each communication round of FL, the clients update local models based on their own data and upload their local updates via wireless channels.