no code implementations • 12 Apr 2024 • Cui Zhang, Xiao Xu, Qiong Wu, Pingyi Fan, Qiang Fan, Huiling Zhu, Jiangzhou Wang
In this scheme, vehicle s mobility, channel conditions with temporal variations, computational resources with temporal variations, different data amount, transmission channel status of vehicles as well as Byzantine attacks were taken into account. Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.
1 code implementation • 10 Mar 2024 • Qiong Wu, Le Kuai, Pingyi Fan, Qiang Fan, Junhui Zhao, Jiangzhou Wang
In Internet of Things (IoT) networks, the amount of data sensed by user devices may be huge, resulting in the serious network congestion.
1 code implementation • 18 Jan 2024 • Qiong Wu, Wenhua Wang, Pingyi Fan, Qiang Fan, Huiling Zhu, Khaled B. Letaief
Finally, we propose a multi-agent deep reinforcement learning (MADRL) based algorithm to decide where the predicted popular contents are collaboratively cached among SBSs.
no code implementations • 30 Nov 2023 • Qiong Wu, Wenhua Wang, Pingyi Fan, Qiang Fan, Jiangzhou Wang, Khaled B. Letaief
Vehicular edge computing (VEC) is a promising technology to support real-time vehicular applications, where vehicles offload intensive computation tasks to the nearby VEC server for processing.
no code implementations • 5 May 2023 • Yuchen Shi, Zheqi Zhu, Pingyi Fan, Khaled B. Letaief, Chenghui Peng
Federated Learning (FL) is a promising distributed learning mechanism which still faces two major challenges, namely privacy breaches and system efficiency.
no code implementations • 6 Apr 2023 • Qiong Wu, Siyuan Wang, Pingyi Fan, Qiang Fan
Furthermore, as vehicles have different local training time due to various sizes of local data and their different computing capabilities, asynchronous federated learning (AFL) is employed to facilitate the RSU to update the global model immediately after receiving a local model to reduce the aggregation delay.
1 code implementation • 31 Mar 2023 • Anbai Jiang, Wei-Qiang Zhang, Yufeng Deng, Pingyi Fan, Jia Liu
Automatic detection of machine anomaly remains challenging for machine learning.
no code implementations • 11 Mar 2023 • Hongbiao Zhu, Qiong Wu, Qiang Fan, Pingyi Fan, Jiangzhou Wang, Zhengquan Li
It is critical to determine the optimal policy including sample collection requirements and power allocation to minimize the AoI and energy consumption of MIMO-NOMA IoT system, where the transmission rate is not a constant in the SIC process and the noise is stochastic in the MIMO-NOMA channel.
1 code implementation • 11 Mar 2023 • Zheqi Zhu, Yuchen Shi, Jiajun Luo, Fei Wang, Chenghui Peng, Pingyi Fan, Khaled B. Letaief
By adopting layer-wise pruning in local training and federated updating, we formulate an explicit FL pruning framework, FedLP (Federated Layer-wise Pruning), which is model-agnostic and universal for different types of deep learning models.
no code implementations • 5 Oct 2022 • Zheqi Zhu, Pingyi Fan, Chenghui Peng, Khaled B. Letaief
Then, we formulate the problem of selecting optimal IS weights and obtain the theoretical solutions.
1 code implementation • 3 Aug 2022 • Siyuan Wang, Qiong Wu, Qiang Fan, Pingyi Fan, Jiangzhou Wang
For the traditional federated learning (FL), vehicles train the data locally to obtain a local model and then upload the local model to the RSU to update the global model, thus the data privacy can be protected through sharing model parameters instead of data.
1 code implementation • 2 Aug 2022 • Qiong Wu, Yu Zhao, Qiang Fan, Pingyi Fan, Jiangzhou Wang, Cui Zhang
In addition, we consider the mobility of vehicles and propose a deep reinforcement learning algorithm to obtain the optimal cooperative caching location for the predicted popular contents in order to optimize the content transmission delay.
no code implementations • 25 Mar 2022 • Rui She, Pingyi Fan
The information metric, e. g. Kullback-Leibler divergence in the original GAN, makes the objective function have different sensitivity on different event probability, which provides an opportunity to refine GAN-based anomaly detection by influencing data generation.
no code implementations • 2 Dec 2021 • Shuo Wan, Jiaxun Lu, Pingyi Fan, Yunfeng Shao, Chenghui Peng, Khaled B. Letaief
In this paper, we develop a vertical-horizontal federated learning (VHFL) process, where the global feature is shared with the agents in a procedure similar to that of vertical FL without any extra communication rounds.
no code implementations • 30 Apr 2021 • Shuo Wan, Jiaxun Lu, Pingyi Fan, Yunfeng Shao, Chenghui Peng, Khaled B. Letaief
Federated learning (FL) has recently emerged as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets.
no code implementations • 2 Jan 2021 • Pingyi Fan
It is expected that the developed new type Hoeffding's inequalities could get more interesting applications in some related fields that use Hoeffding's results.
no code implementations • 28 Dec 2020 • Zheqi Zhu, Shuo Wan, Pingyi Fan, Khaled B. Letaief
To the best of our knowledge, it's the first joint MEC collaboration algorithm that combines the edge federated mode with the multi-agent actor-critic reinforcement learning.
1 code implementation • 11 Dec 2020 • Gangtao Xin, Pingyi Fan
Soft compression is a lossless image compression method, which is committed to eliminating coding redundancy and spatial redundancy at the same time by adopting locations and shapes of codebook to encode an image from the perspective of information theory and statistical distribution.
1 code implementation • 2 Dec 2020 • Qiong Wu, Hanxu Liu, Ruhai Wang, Pingyi Fan, Qiang Fan, Zhengquan Li
Furthermore, the long-term reward of the system (i. e., jointly considers the transmission delay, computing delay, available resources, and diversity of vehicles and tasks) becomes a significantly important issue for providers.
Networking and Internet Architecture
1 code implementation • 5 Nov 2020 • Qiong Wu, Hongmei Ge, Pingyi Fan, Jiangzhou Wang, Qiang Fan, Zhengquan Li
However, one vehicle in platoons inevitably suffers from a disturbance resulting from the leader vehicle acceleration/deceleration, wind gust and uncertainties in a platoon control system, i. e., aerodynamics drag and rolling resistance moment etc.
Networking and Internet Architecture
no code implementations • 25 Mar 2020 • Rui She, Pingyi Fan
As for the original GAN, there exist drawbacks for its hidden information measure based on KL divergence on rare events generation and training performance for adversarial networks.
no code implementations • 5 May 2019 • Shuo Wan, Jiaxun Lu, Pingyi Fan, Khaled B. Letaief
In this paper, a MEC-based big data analysis network is discussed.
no code implementations • 9 Mar 2019 • Zheqi Zhu, Pingyi Fan
With the rapid growth of the data volume and the fast increasing of the computational model complexity in the scenario of cloud computing, it becomes an important topic that how to handle users' requests by scheduling computational jobs and assigning the resources in data center.
no code implementations • 19 Jul 2018 • Qiong Wu, Shuzhen Nie, Pingyi Fan, Zhengquan Li, Cui Zhang
In the second step, we first set the minimum average one-hop delay found in the first step as the initial optimization goal and then adopt the swarming approach again to get the one-hop delay of each backbone vehicle balance to the minimum average one-hop delay.
Networking and Internet Architecture